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  Subjects -> AERONAUTICS AND SPACE FLIGHT (Total: 124 journals)
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IEEE Transactions on Aerospace and Electronic Systems
Journal Prestige (SJR): 0.611
Citation Impact (citeScore): 3
Number of Followers: 330  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0018-9251
Published by IEEE Homepage  [228 journals]
  • IEEE Aerospace and Electronic Systems Society Information

    • Free pre-print version: Loading...

      Pages: C2 - C2
      Abstract: null
      PubDate: TUE, 10 OCT 2023 09:17:50 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Automotive Imaging and Super-Resolution Radar Systems

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      Authors: I. Bilik;J. Tabrikian;F. Gini;
      Pages: 4780 - 4781
      Abstract: Road traffic accidents caused approximately 1.35 million deaths worldwide in 2016. Active driver assistance systems (ADASs) have been shown to dramatically reduce traffic accidents and causalities. The current global ADAS market of $27 billion is expected to grow to $83 billion by 2030. Multiple active safety features and some level of autonomy have already been introduced in the majority of commercial vehicles, and the automotive industry is progressing toward autonomous driving. An autonomous vehicle substitutes human drivers in both sensing and decision making. Therefore, it needs to collect information on the host's vehicle location and its surroundings. The situation awareness sensors are used to provide reliable, dense, and timely information on the vehicle's surroundings. In particular, they need to acquire information on the vehicle's drivable path and all objects above the road level. Sensors need to detect, localize, and classify objects that could interfere with autonomous driving.
      PubDate: TUE, 10 OCT 2023 09:17:46 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Enhanced Automotive Sensing Assisted by Joint Communication and Cognitive
           Sparse MIMO Radar

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      Authors: Xiangrong Wang;Weitong Zhai;Xuan Zhang;Xianghua Wang;Moeness G. Amin;
      Pages: 4782 - 4799
      Abstract: Automotive radar is mandated to provide high accuracy direction of arrival (DOA) estimation for safe driving, while remaining a low cost device for feasible mass production. Sparse multiple input–multiple output (MIMO) arrays emerge as a primary candidate to meet these requirements. As DOA estimation accuracy is a main indicator of tracking performance, Cramer–Rao bound is chosen as the goodness measurement for sparse MIMO array optimization, but its application requires prior information of the road environment. We propose a cognitive sparse MIMO array automotive radar, which “perceives” the road environment via automotive sensing supplemented by coexisted communication from roadside unit to vehicle. This information is used for codesigning a sparse MIMO array for enhanced automotive sensing and vehicle-to-roadside unit (V2R) communication. Note that both static roadside unit (RSU) and dynamic RSU are usually deployed in Internet of Vehicles, which can provide continuous transmission coverage and permanent connectivity. The bidirectional communications are integrated into the automotive radar. This is achieved by joint transmit waveform design with spectral nulls for communications and with shared sparse MIMO array codesign for both sensing and high quality V2R communications. Simulation results validate the enhanced automotive sensing performance assisted by the integrated bidirectional communications in the cycle of cognitive-driven optimization.
      PubDate: MON, 01 MAY 2023 10:17:19 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Probabilistic SAR Processing for High-Resolution Mapping Using
           Millimeter-Wave Radar Sensors

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      Authors: Timo Grebner;Alexander Grathwohl;Pirmin Schoeder;Vinzenz Janoudi;Christian Waldschmidt;
      Pages: 4800 - 4814
      Abstract: In the field of autonomous driving, highly accurate representations of the environment are essential for trajectory planning as well as for estimating the vehicle's location. Today, this can be achieved with the help of chirp-sequence radar sensors or radar sensor networks. The possibilities for environmental mapping cover simple point clouds, target-list-based grid maps, and raw data-based high-resolution synthetic aperture radar (SAR) maps. While for target-list-based grid maps, it has already been shown that a probabilistic occupancy grid map has significant advantages over an amplitude-based grid map in terms of robustness and resolution, and no probabilistic approaches to SAR processing exist up to now. This article presents a fundamental approach of processing radar raw data to generate high-resolution SAR images based on probabilities. A probabilistic SAR processing is presented that combines high-resolution environmental mapping with amplitude-independent target detection. Based on the measurements, a qualitative and quantitative comparison between conventional amplitude and phase-based SAR processing, the presented probabilistic SAR processing, and a probabilistic target-list-based occupancy grid map is performed. Since the presented algorithm is not limited to the automotive field and chirp-sequence radar sensors, it can be extended to arbitrary SAR applications and radar architectures.
      PubDate: TUE, 27 JUN 2023 10:01:35 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Deep-Neural-Network-Enabled Vehicle Detection Using High-Resolution
           Automotive Radar Imaging

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      Authors: Ruxin Zheng;Shunqiao Sun;Hongshan Liu;Teresa Wu;
      Pages: 4815 - 4830
      Abstract: Advanced driver assistance systems (ADASs) and autonomous vehicles rely on different types of sensors, such as camera, radar, ultrasonic, and LiDAR, to sense the surrounding environment. Compared with the other types of sensors, millimeter-wave automotive radar has advantages in terms of cost and reliability under bad weather conditions (e.g., snow, rain, and fog) and does not suffer from light condition variations (e.g., darkness). Typical radar devices used in today's commercial vehicles with ADAS features produce sparse point clouds in low angular resolution with a limited number of antennas. In this article, we present a machine-learning-aided signal processing chain to suppress the radar imaging blur effect introduced by the phase migration in time-division multiplexing multiple-input multiple-output radar, to generate low-level high-resolution radar bird's-eye view (BEV) spectra with rich object's features. Compared with radar point clouds, there is no information loss in radar BEV spectra. We then propose a temporal-fusion distance-tolerant single-stage object detection network, termed as TDRadarNet, and an enhanced version, TDRadarNet+, to robustly detect vehicles in both long and short ranges on radar BEVs. We introduce a first-of-its-kind multimodel dataset, containing 14 800 frames of high-resolution low-level radar BEV spectra with synchronized stereo camera RGB images and 3-D LiDAR point clouds. Our dataset achieves 0.39-m range resolution and $\text{1.2}^\circ$ degree azimuth angular resolution with 100-m maximum detectable range. Moreover, we create a subdataset, the Doppler Unfolding dataset, containing 244 140 beam vectors extracted from the 3-D radar data cube. With extensive testing and evaluation, we demonstrate that our Doppler unfolding network achieves 93.46% Doppler unfolding accuracy. Compared to YOLOv7, a state-of-the-art image-based object detection network, TDRadarNet, achieves a 70.3% average precision (AP) for vehicle detection, demonstrating a 21.0% improvement; TDRadarNet+ achieves a 73.9% AP, showing a 24.6% improvement in performance.
      PubDate: MON, 15 MAY 2023 10:02:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • MCRB on DOA Estimation for Automotive MIMO Radar in the Presence of
           Multipath

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      Authors: Moshe Levy-Israel;Igal Bilik;Joseph Tabrikian;
      Pages: 4831 - 4843
      Abstract: Autonomous driving and advanced active safety features require accurate high-resolution sensing capabilities. Automotive radars are the key component of the vehicle sensing suit. However, when these radars operate in proximity to flat surfaces, such as roads and guardrails, they experience a multipath phenomenon that can degrade the accuracy of the direction-of-arrival (DOA) estimation. Presence of multipath leads to misspecification in the radar data model, resulting in estimation performance degradation, which cannot be reliably predicted by conventional performance bounds. In this article, the misspecified Cramér–Rao bound (MCRB), which accounts for model misspecification, is derived for the problem of DOA estimation in the presence of multipath which is ignored by the estimator. Analytical relations between the MCRB and the Cramér–Rao bound are established, and the DOA estimation performance degradation due to multipath is investigated. The results show that the MCRB reliably predicts the asymptotic performance of the misspecified maximum-likelihood estimator and therefore, can serve as an efficient tool for automotive radar performance evaluation and system design.
      PubDate: TUE, 13 JUN 2023 10:01:08 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Perfect Hash-Based Routing Lookup for LEO Constellation Backbone Network

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      Authors: Keran Zhang;Lei Tang;Chao Zhao;Sheng Zhong;Hangzai Luo;
      Pages: 4844 - 4857
      Abstract: The performance of packet forwarding mainly relies on the routing lookup (RL) in high-speed routers and switches. Due to the network topology change and the limited resource on aerospace devices, the RL strategy for low Earth orbiting (LEO) constellation backbone networks should be specifically designed. Targeting lower resource consumption, we first propose the perfect hash-based RL, including a perfect hash function (PHF) for metarule space compression plus random access for the output interface. A satellite perfect hash function (SPHF) algorithm is proposed to generate efficient hash functions with limited storage. For 10 000 randomized keys, the SPHF algorithm constructs 50-Kb PHFs in 3.05 ms with 538 Kb storage. Then, we design the routing-oriented hash function (RHF) algorithm by fusing the perfect hash and RL, and optimize RHF toward the hardware implementation. The RHF algorithm is robust to different RL scenarios. In the field programmable gate array (FPGA) (xc7k70t-fbv900-1) experiment with 10 000 32-bit metarules and four output interfaces, a typical RHF algorithm (RHF1) costs only 13.2 ns and 1429 equivalent look-up-table (LUTs) to lookup each packet under 312.5 MHz clock frequency. Compared with most advanced ternary content addressable memory (TCAM)-based implementations, RHF1 reduces the storage and logic resources by at least an order of magnitude while keeping comparable lookup latency in the same FPGA Chip. This article provides a design mentality for the RL of low Earth orbiting constellation backbone networks.
      PubDate: FRI, 03 MAR 2023 10:01:28 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Advanced Warning of Threatening Equatorial Plasma Bubbles to Support GBAS
           in Low Latitudes

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      Authors: Leonardo Marini-Pereira;Alison de Oliveira Moraes;Sam Pullen;
      Pages: 4858 - 4869
      Abstract: In low-latitude regions such as Brazil, the single-frequency ground-based augmentation system (GBAS) is not yet fully operational due to the influence of ionospheric variability. The most critical issue for GBAS is the occurrence of ionospheric gradients large enough to compromise the system's integrity. This can result in unsafe operation because a single GBAS ground station cannot identify all threatening gradients in real time. This article develops and validates a strategy to detect and alert in advance the presence and effects of equatorial plasma bubbles (EPBs), which create the largest threat to GBAS operations in low latitudes. This alerting system uses surrounding stations in the vicinity of the GBAS facilities being supported to monitor the ionosphere state in real time and send alerts to the served GBAS facility when threatening conditions are detected on one or more tracked satellites. Time-step measurements of ionospheric gradients and data availability at these surrounding stations are used to generate these alerts when needed. An architecture is proposed for such a system along with a complete validation of the method. Validation uses a reliable dataset from the peak of ionospheric Solar Cycle 24 containing postprocessed station-pair gradients in time, amplitude scintillation indices, and ionospheric maps. All gradients from the dataset classified as threatening to GBAS operation were encompassed by the alerts issued by the real-time methodology within the adopted success criteria. Based on these results, an EPB alerting approach based on this method presented and validated in this article can significantly enhance single-frequency GBAS integrity and availability in low latitudes.
      PubDate: THU, 09 FEB 2023 10:01:54 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • ROEKF-MPC Estimator for Satellite Attitude and Gyroscope Bias Estimation

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      Authors: Mihindukulasooriya Sheral Crescent Tissera;Kai Jie Ethan Foo;Kay-Soon Low;Shu Ting Goh;Rongde Darius Tan;
      Pages: 4870 - 4882
      Abstract: Microelectromechanical system (MEMS) gyroscopes are commonly used for satellite attitude determination and control system in recent years. They are packaged in a small form factor and have lower power consumption. They provide a low-cost solution to the emerging NewSpace industry. MEMS gyroscopes exhibit time-zero null bias with variation over temperature. To overcome this problem, this article proposes a technique for self-calibration of gyroscope bias based on a reduced-order extended Kalman filter (EKF) working alongside an estimator based on the model predictive control (MPC) approach. The proposed technique is referred to as the ROEKF-MPC estimator. Both simulation results and experimental verification using an in-house developed spacecraft simulator are presented. Unlike the EKF method, the proposed method can estimate the gyroscope bias instantly and it is robust against changes in temperature. In addition, the proposed method employing a reduced-order EKF is 28.4% computationally more efficient than the typical higher order EKF method. Results show that the pointing performance is better than 0.35° making the proposed method very attractive for most NewSpace applications. While the proposed method allows for self-calibration of gyroscope bias, its performance is affected by the gyroscope noise and requires the satellite's attitude to be nearly in a steady state. This limitation will be addressed in detail.
      PubDate: WED, 15 FEB 2023 10:03:19 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Demand-Aware Onboard Payload Processor Management for High-Throughput NGSO
           Satellite Systems

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      Authors: Tedros Salih Abdu;Steven Kisseleff;Eva Lagunas;Joël Grotz;Symeon Chatzinotas;Björn Ottersten;
      Pages: 4883 - 4899
      Abstract: High-throughput satellite (HTS) systems with digital payload technology have been identified as a key enabler to support 5G/6G high-data connectivity with wider coverage area. The satellite community has extensively explored resource allocation methods to achieve this target. Typically, these methods do not consider the intrinsic architecture of the flexible satellite digital payload, which consists of multiple processors responsible for receiving, processing, and transmitting the signals. This article presents a demand-aware onboard processor management scheme for broadband nongeostationary satellites. In this context, we formulate an optimization problem to minimize the number of active onboard processors while meeting the system constraints and user requirements. As the problem is nonconvex, we solve it in two steps. First, we transform the problem into demand-driven bandwidth allocation while fixing the number of processors. Second, using the bandwidth allocation solution, we determine the required number of processors with two methods: 1) sequential optimization with the branch-and-bound method and 2) bin packing with next-fit, first-fit, and best-fit methods. Finally, we demonstrate the proposed methods with extensive numerical results. It is shown that the branch-and-bound, best-fit, and first-fit methods manage the processors better than the next-fit method. Furthermore, branch-and-bound method requires fewer processors than the above methods.
      PubDate: WED, 15 FEB 2023 10:03:19 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Classification Schemes for the Radar Reference Window: Design and
           Comparisons

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      Authors: Chaoran Yin;Linjie Yan;Chengpeng Hao;Silvia Liberata Ullo;Gaetano Giunta;Alfonso Farina;Danilo Orlando;
      Pages: 4900 - 4918
      Abstract: In this article, we address the problem of classifying data within the radar reference window in terms of statistical properties. Specifically, we partition these data into statistically homogeneous subsets by identifying possible clutter power variations with respect to the cells under test (accounting for possible range-spread targets) and/or clutter edges. To this end, we consider different situations of practical interest and formulate the classification problem as multiple-hypothesis tests comprising several models for the operating scenario. Then, we solve the hypothesis testing problems by resorting to suitable approximations of the model-order selection rules due to the intractable mathematics associated with the maximum likelihood estimation of some parameters. Remarkably, the classification results provided by the proposed architectures represent an advanced clutter map since, besides the estimation of the clutter parameters, they contain a clustering of range bins in terms of homogeneous subsets. In fact, such information can drive the conventional detectors toward more reliable estimates of the clutter covariance matrix according to the position of the cells under test. The performance analysis confirms that the conceived architectures represent a viable means to recognize the scenario wherein the radar is operating at least for the considered simulation parameters.
      PubDate: WED, 15 FEB 2023 10:03:19 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Formation-of-Arrays Antenna Technology for High-Throughput Mobile
           Nonterrestrial Networks

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      Authors: Giacomo Bacci;Riccardo De Gaudenzi;Marco Luise;Luca Sanguinetti;Elena Sebastiani;
      Pages: 4919 - 4935
      Abstract: Effective integration of terrestrial and nonterrestrial segments is one of the key research avenues in the design of current and future wireless communication networks. To this aim, modern communication-satellite constellations attain sufficiently high throughput in terms of bit rate per unit area on the ground by rather aggressive patterns of spatial frequency reuse. This goal calls for on-board narrow-beam antennas, whose size turns out to be in many cases incompatible with the size/mass and accommodation constraints of the hosting satellite. This article investigates the attainable performance of large distributed arrays of antennas implemented as the ensemble of a few to many simpler subantennas of smaller sizes, carried by one (small) satellite each. The subantennas can in their turn be implemented like (regular) 2-D arrays of simple radiating elements, realizing an overall (distributed) antenna architecture that we call “formation of arrays” (FoA). The satellites that implement this radiating architecture need to be relatively close to each other and constitute a formation of flying objects, to be coordinated and controlled as a whole. In this article, we develop a theoretical analysis of an FoA antenna, and we show how to take advantage of this new technology to improve network throughput in a multibeam S-band mobile communication network with low-Earth or geostationary orbiting satellites directly providing 5G-like communication services to hand-held user terminals.
      PubDate: TUE, 14 FEB 2023 10:02:37 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Event-Based Predefined-Time Deployment Control for Space Triangular
           Tethered Satellite System With Input Quantization

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      Authors: Bingxiao Huang;Fan Zhang;Mengshi Song;Panfeng Huang;
      Pages: 4936 - 4946
      Abstract: The problem of space triangular tethered satellite system deployment in the presence of external disturbances, input quantization, and limited communication from the controller to the actuator is investigated in this article. A disturbance-based predefined-time sliding mode controller is employed to ensure the system converges to the neighborhood of zero without depending on initial conditions. An input quantizer and an event-triggered mechanism are integrated into the proposed controller to reduce the frequency and bandwidth of data transmission from controller to actuator. An adaptive law is considered to counteract the control input chattering produced by quantization errors and event-triggered input errors. The whole proposed control scheme guarantees that the closed-loop system is semiglobally stable and converges to a stable region at the predefined time. Finally, the effectiveness of the proposed scheme is confirmed by simulation tests.
      PubDate: THU, 16 FEB 2023 10:02:37 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Finite-Thrust Lambert Transfer Based on Multistage Constant-Vector Thrust
           Control

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      Authors: Shengzhou Bai;Yuan Wang;Hanyu Liu;Xiucong Sun;
      Pages: 4947 - 4967
      Abstract: In this work, an efficient multistage constant-vector thrust control algorithm is proposed for spacecraft to achieve finite-thrust Lambert transfer considering J2 perturbation. In contrast to most designs that apply continuous-thrust requiring time-varying direction and the magnitude of the thrust, the proposed control scheme utilizes a multistage constant-vector thrust, which means the modulus and direction of the thrust are constant in each stage determined by a given set of time nodes dividing the whole transfer time, which reduces the control complexity of the engine. Based on the multistage sensitivity matrix, which describes the first-order relationship between the thrust and the change of the orbital elements, a rapid algorithm is presented to obtain the multistage constant-vector thrust solution with the required accuracy. Furthermore, the optimization problem of the multistage constant-vector thrust solution is established, and the direct optimization method is proposed to obtain an optimal multistage constant-vector thrust solution for a given set of time nodes. Based on this approach, the node-sequence optimization method is further proposed to obtain a series of feasible optimized solutions rapidly corresponding to a monotonically increasing time-node sets. Compared with the existing finite-thrust methods, the presented methods balance the fuel consumption and control requirements of the engine because the situation that the engine always needs to change the direction and the magnitude of the thrust could be avoided, which are practical options for engineering applications.
      PubDate: THU, 02 MAR 2023 10:02:37 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Optimal Orbit Design and Mission Scheduling for Sun-Synchronous Orbit
           On-Orbit Refueling System

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      Authors: Peng Han;Yanning Guo;Pengyu Wang;Chuanjiang Li;Witold Pedrycz;
      Pages: 4968 - 4983
      Abstract: This article proposes a two-stage optimization framework to simultaneously solve the orbit design and mission scheduling problems of on-orbit refueling (OOR) systems in Sun-synchronous orbit (SSO). The fuel can be delivered to the client satellite (CS) by small-scale service satellites from the on-orbit fuel station (FS), which leads to a new FS-servicer-CS OOR mode. For the first stage, a modified spectral clustering-nonlinear programming (NLP) method is used to assign CSs to FSs, and design the orbit of FSs under $J_{2}$ perturbation. For the second stage, the mission scheduling problem is formulated as a mixed-integer NLP model and solved via the genetic quantum algorithm. Different from the existing literature, this article introduces a clustering distance metric combining multiple orbit characteristics for CS assignment, which can indirectly reduce the estimated fuel costs in the FS orbit design. Furthermore, the objective of reducing the number of servicers is first considered in the second stage. The effectiveness and superiority of the proposed two-stage framework are verified through several numerical simulations and comparison studies.
      PubDate: FRI, 17 MAR 2023 10:01:51 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • High Maneuvering Target Long-Time Coherent Integration and Motion
           Parameters Estimation Based on Bayesian Compressive Sensing

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      Authors: Lei Yu;Feng He;Qilei Zhang;Yi Su;Yichao Zhao;
      Pages: 4984 - 4999
      Abstract: This article intends to address the long-time coherent integration of high maneuvering targets in low signal-to-noise environments with high computational efficiency and accuracy. The high-order motion parameters estimation, considering the acceleration and jerk, is modeled as the underestimated linear regression and the complex-field Bayesian compressive sensing (BCS) algorithm is introduced to resolve the sparse recovery. To correct the high-order range migration and reduce the order of Doppler frequency migration (DFM), the adjacent cross correlation (ACCF) is applied. An effective method based on noncoherent integration is proposed to extract the self-term from ACCF result. Further, with the proper design of sensing matrix, the acceleration and jerk motion parameters are estimated by sparse reconstruction based on the complex-field BCS. Compared with the traditional methods based on time-frequency transform, like Lv's distribution, the proposed BCS algorithm is free from the interference of cross-terms and maintains the superresolution ability, which provides better performance in multitargets discrimination. Finally, the numerical experiments validate the advantages of the proposed method in motion parameters estimation, superresolution ability, and computational efficiency.
      PubDate: WED, 22 FEB 2023 07:51:31 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Robust Initial Orbit Determination for Surveillance Doppler-Only Radars

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      Authors: Matteo Losacco;Roberto Armellin;Carlos Yanez;Laura Pirovano;Stéphanie Lizy-Destrez;Francesco Sanfedino;
      Pages: 5000 - 5011
      Abstract: An initial orbit determination algorithm for surveillance Doppler-only radars with embedded quantification capabilities is presented. The method is based on a combination of Gauss' and Lambert's solvers formulated in the differential algebra (DA) framework, which provides the Taylor expansion of the state estimate with respect to the measurement uncertainties. This feature makes the approach particularly suitable for handling data association problems. The first part of the article describes the mathematical formulation of the method, while an extensive analysis of its performance and a comparison with a reference algorithm are carried out in the second part, using both simulated and real data. The proposed approach is shown to be accurate and robust and particularly suited to short-arc observations.
      PubDate: MON, 27 FEB 2023 10:11:30 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Dual High-Resolution Correlators for Multipath Mitigation in BOC Signals

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      Authors: Zhenyu Tian;Xiaowei Cui;Yonghui Zhu;Mingquan Lu;
      Pages: 5012 - 5026
      Abstract: Multipath is a dominant source of positioning errors in global navigation satellite system receivers. It is difficult to design multipath mitigation techniques for binary-offset-carrier (BOC) signals due to the complicated autocorrelation function (ACF) of BOC signals and localized variations of multipath. In this article, a dual high-resolution correlators (DHRC) technique is proposed for BOC signals to mitigate code and subcarrier multipath errors, as well as the carrier multipath error that is neglected by common techniques. Based on the concept of dual binary phase-shift keying (BPSK) tracking, the BOC signal is decomposed into two BPSK signals located in the upper and lower sidebands, respectively, thereby transforming the multipath mitigation in the BOC signal into the multipath mitigation in the upper and lower sidebands. Two narrow correlation (NC) functions of the upper and lower sidebands are synthesized using multiple correlation values, so the multipath outside the main peak of the synthesized correlation function is eliminated. The peak value of the synthesized correlation function is reduced from the original ACF, resulting in a large noise error. Therefore, a complementary filter is designed to compensate the noise error. Theoretical analysis and simulation results demonstrate that the DHRC technique with complementary filters can significantly mitigate multipath errors for BOC signals with slight thermal noise performance loss.
      PubDate: THU, 16 FEB 2023 10:02:37 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • On Estimating Parameters of a Multicomponent Chirp Model With Equal Chirp
           Rates

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      Authors: Abhinek Shukla;Debasis Kundu;Amit Mitra;Rhythm Grover;
      Pages: 5027 - 5038
      Abstract: Multicomponent chirp signal models with equal chirp rates appear in various radar applications, e.g., synthetic aperture radar, echo signal of a rapid mobile target, etc. Many suboptimal estimators have been developed for such models; however, these suffer from either identifiability or error propagation effect. In this article, we have developed theoretical properties of the least squares estimators (LSEs) of the parameters of the multicomponent chirp model with equal chirp rates, where the model is contaminated with linear stationary errors. We also propose two computationally efficient estimators as alternative to LSEs, namely, sequential combined estimators and sequential plug-in estimators. Strong consistency and asymptotic normality of these estimators have been derived. Interestingly, it is observed that a sequential combined estimator of the chirp rate parameter is asymptotically efficient. Extensive numerical simulations have been performed, which validate satisfactory computational and theoretical performance of all three estimators. We have also analyzed simulated radar data with the help of our proposed estimators of the multicomponent chirp model with equal chirp rates, which performs efficiently in the recovery of inverse synthetic aperture radar image of a target from a noisy data.
      PubDate: THU, 23 FEB 2023 10:03:45 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Airborne Flexible-Array Radar Clutter Characteristics and STAP Performance
           Analysis

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      Authors: Yan Sun;Wen-Qin Wang;
      Pages: 5039 - 5051
      Abstract: Airborne flexible-array (FA) radar has been attracting increased attention due to its promising application potential. As space-time adaptive processing (STAP) plays an important role in moving target indicator applications, this article studies an airborne FA radar framework and its clutter characteristics and STAP performance. Taking deformable structure of the wing FA radar as an example, the corresponding spatial-temporal signal model is formulated with deformation parameters. Next, the spatial-temporal clutter properties of FA radar are analyzed mathematically by exploiting the clutter covariance matrix and Doppler characteristics. Furthermore, one clutter suppression method is proposed by jointly utilizing the compensation algorithm, temporal-dimension reduction filtering, and spatial adaptive processing. Finally, STAP performance analysis for the airborne FA radar is provided. Numerical results verify all the theoretical analyses.
      PubDate: WED, 22 FEB 2023 07:51:31 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Improved Moving Target Detection in Homogeneous and Heterogeneous Clutter
           With Limited Secondary Data Using Unit Circle Roots Constraints

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      Authors: Jared Smith;Arnab K. Shaw;
      Pages: 5052 - 5071
      Abstract: This article proves that the roots of the $z$-polynomial corresponding to the ideal adaptive matched filter (AMF) lie on the unit circle (UC). Motivated by this proof, we propose UC roots-constrained AMF (UCRC-AMF), which enforces this fundamental UC roots property while maximizing the SINR to determine the detector polynomials. The UCRC algorithm exploits polynomial conjugate symmetry and applies it to the problem of radar moving target detection (MTD) in homogeneous and heterogeneous clutter. The algorithm splits the multidimensional AMF optimization problem into multiple 1-D AMF problems. Simulation studies show that the UCRC-based AMF for Gaussian clutter, as well as UCRC versions of NAMF and $\alpha$-AMF for compound Gaussian clutter significantly outperform existing state-of-the-art detection algorithms in the presence of limited secondary data and the performance gain is persistent for larger secondary data as well. This work will show that the combined enforcement of UC roots property on detector polynomials and complex-persymmetry of covariance matrices is most effective for superior detection performance. It is also shown that the proposed UCRC-AMF outperforms the existing methods when there is a mismatch with respect to the true target velocity and ideal covariance matrix structure. In addition, it will be demonstrated via simulation that the proposed UCRC-based detectors have approximate constant false alarm rate (CFAR) with respect to the unknown covariance matrix.
      PubDate: MON, 27 FEB 2023 10:11:30 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Cognitive Conformal Subaperturing FDA-MIMO Radar for Power Allocation
           Strategy

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      Authors: Zihang Ding;Junwei Xie;Liyun Yang;
      Pages: 5072 - 5083
      Abstract: Recently, due to its flexible element configuration and more diverse signal processing methods, the conformal array radar has begun to attract wider attention than the conventional array radar. During development of the jamming equipment, the electromagnetic environment becomes more complex, which brings more challenges to the radar system. To overcome the variable interference environment's negative influence on moving target tracking, we propose two adaptive power allocation methods based on two criteria to improve the target tracking accuracy. First, we build the data model of the conformal subaperturing frequency diverse array with multi-input and multi-output radar and the interference signal. Moreover, the Bayesian Cramer–Rao bound (BCRB) and the signal-to-interference-plus-noise ratio (SINR) are derived and taken as the objective functions in proposed optimization problems. Based on this, we build two different power allocation optimization problems under BCRB minimization criterion and SINR maximization criterion, respectively, and two different power constraints are considered. Finally, simulation results verify the effectiveness of the proposed adaptive power allocation methods. The power allocation strategies under two criteria can obtain better tracking accuracy compared with uniform allocation, and the tracking accuracy of the adaptive power allocation method can be influenced by different power constraint conditions.
      PubDate: THU, 23 FEB 2023 10:03:45 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Fast Adaptive Spectrum Sensing Using Hardware Optimized, Cell Averaging
           Estimation for Cognitive Radio and Radar Applications

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      Authors: Rylee G. Mattingly;Justin G. Metcalf;
      Pages: 5084 - 5096
      Abstract: This article describes a technique that enables real-time spectrum sharing by rapidly sensing the spectrum and selecting available frequencies that a system may use for operation. Hardware optimized, cell averaging estimation (HO-CAE) is a method that uses a snapshot of the spectral environment to calculate a dynamic decision threshold that can be used by the fast spectrum sensing (FSS) algorithm to detect available spectrum. This energy detection technique is adaptive as it calculates a new threshold for each spectrum snapshot. A statistical derivation of HO-CAE will be provided with an analysis of probability of false alarm and probability of detection performance for noise only cases and in the presence of a 5G downlink signal. Efficient field programmable gate array implementations of HO-CAE and FSS that enable real-time operation are presented.
      PubDate: MON, 06 MAR 2023 10:02:01 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Multispectrally Constrained Low-PAPR Waveform Optimization for MIMO Radar
           Space-Time Adaptive Processing

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      Authors: Da Li;Bo Tang;Lei Xue;
      Pages: 5097 - 5110
      Abstract: This article focuses on the joint design of transmit waveforms and receive filters for airborne multiple-input–multiple-output radar systems in spectrally crowded environments. The purpose is to maximize the output signal-to-interference-plus-noise-ratio in the presence of signal-dependent clutter. To improve the practicability of the radar waveforms, both a multispectral constraint and a peak-to-average-power ratio constraint are imposed. A cyclic method is derived to iteratively optimize the transmit waveforms and receive filters. In particular, to tackle the encountered nonconvex constrained fractional programming in designing the waveforms (for fixed filters), we resort to the Dinkelbach's transform, minorization–maximization, and leverage the alternating direction method of multipliers. We highlight that the proposed algorithm can iterate from an infeasible initial point and the waveforms at convergence not only satisfy the stringent constraints, but also attain superior performance.
      PubDate: THU, 23 FEB 2023 10:03:45 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Geometric Arithmetic Mean Method for Low Altitude Target Elevation Angle
           Tracking

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      Authors: Mohammad Ali Sebt;Mahdi Goodarzi;Hossein Darvishi;
      Pages: 5111 - 5119
      Abstract: In tracking operation, the signal reflected from the target comes to the antenna by way of two paths. The first is the direct path, from the target to the antenna, and the second is the indirect path, due to the multipath phenomenon, from the target to the surface then to the antenna. In a low altitude situation, the indirect path can cause severe errors in tracking operation as it enters the antenna main beam and combines with the direct path signal. In such a scenario, the antenna fails to track the elevation angle by the target's image located under the ground. A significant challenge in tracking systems is to mitigate the multipath effect. To do so, we extend the elevation geometric mean method and present the target and image elevation angles separation method. Two monopulse equations are proposed to calculate the target elevation angle. The proposed method offers low computational load and can be implemented on monopulse system. The complete noise analysis is derived for the method. It is shown that the error of the proposed method reaches the Carmer Rao lower bound by proper selection of frequencies. Simulation results show accurate target tracking in a multipath environment.
      PubDate: FRI, 24 FEB 2023 10:06:28 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Bias-Reduced Solution for Multistatic Localization Using Differential
           Delays and Doppler Shifts

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      Authors: Qinke Qi;Youming Li;Qiang Guo;
      Pages: 5120 - 5133
      Abstract: In this article, the multistatic localization problem with unknown propagation speed is investigated using differential delays and Doppler shifts between the signals from direct and indirect paths. A series of pseudolinear equations is formulated via the transformation of measurement models. A weighted least squares (WLS) formulation is then proposed after ignoring the second-order error terms, which can be rewritten as a nonconvex optimization problem with the relationships among variables included as constraints. To deal with the nonconvexity of the problem, semidefinite relaxation is applied, resulting in a convex semidefinite program (SDP). Several reasonable second-order cone constraints constructed via basic inequality and Cauchy–Schwarz inequality are added to tighten the relaxed SDP problem. By preserving the second-order error terms in equations, the bias of the estimate from the WLS formulation is also derived and then subtracted to nearly eliminate the bias and reach a bias-reduced solution. Simulation results show that the mean square error of the proposed method approaches the Cramer–Rao lower bound, and the bias is reduced significantly.
      PubDate: WED, 22 FEB 2023 07:51:31 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • UAV-Enabled Mobile Edge Computing for Resource Allocation Using
           Cooperative Evolutionary Computation

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      Authors: Shidrokh Goudarzi;Seyed Ahmad Soleymani;Wenwu Wang;Pei Xiao;
      Pages: 5134 - 5147
      Abstract: Edge computing is a viable paradigm for supporting Industrial Internet of Things deployment by shifting computationally demanding tasks from resource-constrained devices to powerful edge servers. In this study, mobile edge computing (MEC) services are provided for multiple ground mobile nodes (MNs) through a time-division multiple access protocol using unmanned aerial vehicle (UAV)-enabled edge servers. Remotely controlled UAVs can serve as MEC servers due to their adaptability and flexibility. However, the current MEC approaches have proven ineffective in situations where the number of MNs rapidly increases or network resources are sparsely distributed. Furthermore, suitable accessibility across wireless networks via MNs with an acceptable quality of service is a fundamental problem for conventional UAV-assisted communications. To tackle this issue, we present an optimized computation resource allocation model using cooperative evolutionary computation to solve the joint optimization problem of queue-based computation offloading and adaptive computing resource allocation. The developed method ensures the task computation delay of all MNs within a time block, optimizes the sum of MN's accessibility rates, and reduces the energy consumption of the UAV and MNs while meeting task computation restrictions. Moreover, we propose a multilayer data flow processing system to make full use of the computational capability across the system. The top layer of the system contains the cloud center, the middle layer contains the UAV-assisted MEC servers, and the bottom layer contains the mobile devices. Our numerical analysis and simulation results prove that the proposed scheme outperforms conventional techniques such as equal offloading time allocation and straight-line flight.
      PubDate: FRI, 03 MAR 2023 10:01:28 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Feasibility Analysis of Optical UAV Detection Over Long Distances Using
           Robotic Telescopes

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      Authors: Denis Ojdanić;Andreas Sinn;Christopher Naverschnigg;Georg Schitter;
      Pages: 5148 - 5157
      Abstract: In recent years, substantial technological development has made unmanned aerial vehicles (UAVs) more versatile, cheaper, and accessible to the public. Alongside many positive effects and use cases, safety concerns are increasing as a plethora of incidents demonstrate the destructive potential of UAVs. To counteract this development and, thus, protect people and critical infrastructure, UAV detection, tracking, and defense has gained more and more research attention. Whereas different drone detection technologies such as RADAR, radio frequency, and acoustic detection are deployed within multispectral systems, optical detection and imaging of approaching objects provide key information to correctly assess the situation. As reaction time is a crucial parameter for successful UAV defense, the operating distance of the optical detection system needs to be improved further. This article presents the analysis, development, and evaluation of a telescope-based UAV detection system. The system consists of a high-precision mount and a telescope equipped with a camera. UAVs are detected in the captured video frames by the deep learning algorithm YOLOv4 using a modified architecture. The proposed system, which uses an $f$/10 telescope with a focal length of $f$ = 2540 mm and a camera equipped with a 7.3 mm × 4.1 mm sensor, allows a significant increase in the optical detection range to more than 3 km of UAVs down to 0.3 m in diameter under daylight conditions and sufficient contrast, extending the reaction time significantly for counter UAV systems.
      PubDate: FRI, 24 FEB 2023 10:06:28 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Saturated Attitude Control of Multispacecraft Systems on SO(3) Subject to
           Mixed Attitude Constraints With Arbitrary Initial Attitude

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      Authors: Zeyu Kang;Qiang Shen;Shufan Wu;Christopher J. Damaren;
      Pages: 5158 - 5173
      Abstract: In this article, for multispacecraft systems (MSSs) with a directed complete communication topology and a time-varying virtual leader, an adaptive saturated attitude controller is proposed to achieve attitude consensus and attitude tracking under arbitrary initial attitude, mixed attitude constraints, input saturation, and external disturbances. First, considering the time-varying desired attitude provided by the virtual leader in a directed complete topology, an MSS attitude error function and an MSS attitude error dynamics based on SO(3) are developed. Next, an effective mixed potential function for the MSS on SO(3) is proposed for the static attitude-forbidden zones, the relative dynamic attitude-forbidden zones and the attitude-mandatory zones. Unlike the existing potential functions, the proposed mixed potential function is suitable for arbitrary initial attitude of the spacecraft in MSS, relaxing the restriction on the initial attitude associated with each static and dynamic attitude constraint zones. Then, an adaptive saturated attitude controller is designed to realize attitude consensus and tracking for the MSSs on SO(3) under arbitrary initial attitude, mixed attitude constraints, saturation constraints, and external disturbances. Finally, simulation results of an MSS with a time-varying virtual leader are demonstrated to illustrate the efficiency of the proposed attitude controller.
      PubDate: FRI, 03 MAR 2023 10:01:28 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Data-Driven Probabilistic Methodology for Aircraft Conflict Detection
           Under Wind Uncertainty

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      Authors: Jaime de la Mota;María Cerezo-Magaña;Alberto Olivares;Ernesto Staffetti;
      Pages: 5174 - 5186
      Abstract: Assuming the availability of a reliable aircraft trajectory planner, this article presents a probabilistic methodology to detect conflicts between aircraft in the cruise phase of flight in the presence of wind velocity forecasting uncertainty. This uncertainty is quantified by ensemble weather forecasts, the members of which are regarded as realizations of correlated random processes and used to derive the eastward and northward components of the wind velocity. First, the Karhunen–Loève (KL) expansion is used to obtain a series expansion of the components of the wind velocity in terms of a set of uncorrelated random variables and deterministic coefficients. Then, the uncertainty generated by these uncorrelated random variables in the outputs of the aircraft trajectory planner is quantified using the arbitrary polynomial chaos technique. Finally, the probability density function of the great circle distance between each pair of aircraft is derived from the polynomial expansions using a Gaussian kernel density estimator and used to estimate the probability of conflict. The arbitrary polynomial chaos technique allows the effects of uncertainty in complex nonlinear dynamical systems, such as those underlying aircraft trajectory planners, to be quantified with high computational efficiency, only requiring the existence of a finite number of statistical moments of the random variables of the KL expansion while avoiding any assumptions on their probability distributions. To demonstrate the effectiveness of the proposed conflict detection method, numerical experiments are conducted via an optimal control-based aircraft trajectory planner for a given wind velocity forecast represented by an ensemble prediction system.
      PubDate: WED, 01 MAR 2023 10:03:39 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Optimized Selective Activations for Networked Filtering

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      Authors: Chao Wan;Yongxin Gao;X. Rong Li;
      Pages: 5187 - 5202
      Abstract: This article studies distributed state estimation for a dynamic system based on measurements collected by every node in a sensor network. Depending on the communications among nodes, information spreads across the network node by node through iterations. Then, nodes work collaboratively to estimate the system state timely. Most of the existing networked filters aim to make all nodes reach consensus at the centralized estimation with sufficient communications. To accommodate limited communication resources in practice, this article proposes to optimize the entire network's estimation accuracy given in a fixed number of communications. This is done by optimizing the sequence of node activations for selective communication. We propose two selective activation schemes: link activation (LA) and star activation (SA). In each iteration, they activate a single link and a single star (i.e., a node with all its neighbors), respectively. We develop two iterative distributed filters (DFs): 1) LA based (LA-DF); and 2) SA based (SA-DF). LA-DF and SA-DF possess many important properties. For example, they are unbiased, convergent, stable, and credible. Finally, we analyze the performance of our filters and provide simulation results compared with existing filters to verify the superiority of the proposed filters.
      PubDate: WED, 08 MAR 2023 10:02:06 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Identity-Aware Decision Network Communication Budgeting: Is Who as
           Important as What'

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      Authors: Zachariah Sutton;Peter Willett;Stefano Marano;Yaakov Bar-Shalom;
      Pages: 5203 - 5217
      Abstract: All practical sensing operations must work with quantized data. In some settings, “high-resolution” uniform quantization is used, and data are treated as approximately continuous. The aim of this article is to facilitate “cheap” central decision making by considering extremely low resolution quantization of data sent from distributed sensors. Along with measurement data, each sensor is assumed to have some label value that is relevant to its stochastic measurement model. All measurement and label data are transmitted to the decision maker in the form of discrete “types.” Censoring is also used to control the expected communication cost—each sensor decides locally whether or not to send its data to the decision center based on the value of its label as well as the value of its measurement. In this article, we formalize the test statistic based on censored and quantized data. We also form a metric that is predictive of decision performance. This performance metric can be maximized to obtain optimal censoring and quantization rules. This optimization is demonstrated for a model that assumes passive sensors uniformly distributed in space.
      PubDate: FRI, 03 MAR 2023 10:01:28 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Estimation of the Elementary Chirp Model Parameters

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      Authors: Anjali Mittal;Rhythm Grover;Debasis Kundu;Amit Mitra;
      Pages: 5218 - 5234
      Abstract: In this article, we propose techniques for estimating elementary chirp model parameters, which are encountered in sonar, radar, acoustics, and other areas. We derive asymptotic theoretical properties of least squares estimators and approximate least squares estimators for the one component elementary chirp model. It is proved that the proposed estimators are strongly consistent and follow the normal distribution asymptotically. We also suggest how to obtain proper initial values for these methods. The problem of finding initial values is a difficult one when the number of components in the model is large, or when the signal-to-noise ratio is low, or when two frequency rates are close to each other. We propose sequential procedures to estimate multiple component elementary chirp model parameters. We prove that the theoretical properties of sequential least squares estimators and sequential approximate least squares estimators coincide with those of least squares estimators and approximate least squares estimators, respectively. Further, the asymptotic variances of the proposed estimators attain the Cramér–Rao lower bounds asymptotically when errors are normal random variables and independently and identically distributed. To evaluate the performance of the proposed estimators, numerical experiments are performed. It is observed that the proposed sequential estimators perform well even in situations where least squares estimators do not perform well. We illustrate the performance of the proposed sequential algorithm on bat data.
      PubDate: WED, 08 MAR 2023 10:02:06 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Reinforcement Learning for the Agile Earth-Observing Satellite Scheduling
           Problem

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      Authors: Adam Herrmann;Hanspeter Schaub;
      Pages: 5235 - 5247
      Abstract: This work explores reinforcement learning (RL) for on-board planning and scheduling of an agile Earth-observing satellite (AEOS). In this formulation of the AEOS scheduling problem, a spacecraft in low Earth orbit attempts to maximize the weighted sum of targets collected and downlinked. The AEOS scheduling problem is formulated as a Markov decision process (MDP) where the number of upcoming imaging targets included in the action space is an adjustable parameter to account for clusters of imaging targets with varying priorities. Monte Carlo tree search (MCTS) and supervised learning are used to train a set of agents with varying numbers of targets in the action space. Two backup strategies are explored for MCTS—an incremental averaging operator and a maximization operator. For all backup operators, performance asymptotically increases as the number of targets in the action space approaches the maximum number of available targets. A benchmark is computed with MCTS to determine an upper bound on performance. Furthermore, MCTS is compared with solutions generated by a genetic algorithm. MCTS demonstrates a 2%–5% increase in average reward at 10%–20% of the single-core wall clock time of the genetic algorithm. A search of various neural network hyperparameters is presented, and the trained neural networks are shown to approximate the MCTS policy with three orders of magnitude less execution time. Finally, the trained agents and the genetic algorithm are deployed on varying target densities for comparison purposes and to demonstrate robustness to mission profiles outside of the training distribution.
      PubDate: THU, 02 MAR 2023 10:02:37 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Flight Control of Waverider Vehicles With Fragility-Avoidance Prescribed
           Performance

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      Authors: Xiangwei Bu;Changchun Hua;Maolong Lv;Zhonghua Wu;
      Pages: 5248 - 5261
      Abstract: This article proposes a prescribed performance control (PPC) methodology called fragility-avoidance PPC for waverider vehicles (WVs) with sudden disturbances based on fuzzy neural approximation. We raise the fragile problem associated with the existing PPC, and to remedy this defect, we construct a flexible prescribed funnel that is able to sense the error fluctuation caused by sudden disturbances and, moreover, tackle the fragile problem by automatically adjusting prescribed boundaries. Then, a simplified fuzzy neural approximation framework is presented to reject the unknown nonaffine dynamics of WVs while avoiding the algebraic loop problem. The stability of the closed-loop system is proved via the Lyapunov method, and finally, the effectiveness and superiority of the addressed method are verified by compared simulations.
      PubDate: THU, 02 MAR 2023 10:02:37 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Distributed Estimation With Adaptive Cluster Learning Over Asynchronous
           Data Fusion

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      Authors: Yi Hua;Hongping Gan;Fangyi Wan;Xinlin Qing;Feng Liu;
      Pages: 5262 - 5274
      Abstract: In the electronic information era, the wireless sensor network (WSN) has always been an essential foundation for information collection, processing, and communication. In WSN with multitask estimation, distributed cooperation estimation with cluster learning has always been an attractive topic. When the unknown estimation parameters become complex, some cluster learning algorithms may not work, and their estimation performance could degrade. In addition, the problems of time delay, caused by synchronous data fusion, and different sampling rates between different types of sensors are usually neglected in practical applications. To solve these problems, an unsupervised distributed multitask estimation algorithm with adaptive cluster learning over asynchronous data is proposed to obtain a more accurate estimation. In the proposed algorithm, the time delay and different sampling rates are fully considered and investigated. The mean stability, mean-square convergence, and behavior of adaptive cluster learning are analyzed for the proposed algorithm with asynchronous data. Finally, simulations are provided to demonstrate the robustness and effectiveness of the proposed algorithm.
      PubDate: WED, 08 MAR 2023 10:02:06 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Optimization of Spacecraft Thrusters Configuration Under Fault
           Diagnosability and Recoverability Constraints

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      Authors: Martin Fauré;David Henry;Jérôme Cieslak;Pierre Lachevre;Finn Ankersen;
      Pages: 5275 - 5286
      Abstract: This article investigates the problem of optimal placement (position and orientation) of spacecraft thrusters, under fault diagnosability and fault recoverability constraints. Avionics equipment contamination and plume impingement are also considered. The goal is to find the configuration with the least number of thrusters required for a given spacecraft architecture, so that equipping the control unit with a model-based fault DX and fault-tolerant control solution, able to accommodate any single thruster's fault can be guaranteed. This includes total loss of controllability of the faulty thruster. The proposed solution is a model-based solution in the sense that it is based on the spacecraft attitude and translational dynamics. With the help of the zonotope concept and its so-called $\mathcal H$-representation, it is shown that this problem can be formulated as a nonlinear constrained optimization problem, which can be solved efficiently using hybrid optimization techniques. The proposed solution is assessed on a generic spacecraft architecture that performs a proximity maneuver.
      PubDate: TUE, 14 MAR 2023 10:02:19 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Data-Driven Clustering and Bernoulli Merging for the Poisson
           Multi-Bernoulli Mixture Filter

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      Authors: Marco Fontana;Ángel F. García-Fernández;Simon Maskell;
      Pages: 5287 - 5301
      Abstract: This article proposes a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter to lower its computational complexity and make it suitable for multiple target tracking with a high number of targets. We define a measurement-driven clustering algorithm to reduce the data association problem into several subproblems, and we provide the derivation of the resulting clustered PMBM posterior density via Kullback–Leibler divergence minimization. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and intertrack swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than 1000 targets.
      PubDate: TUE, 07 MAR 2023 10:13:24 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Tracking Control of Quadrotor With Intermittent Target Measurements: A
           Stochastic Switched Systems Approach

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      Authors: Ye Liang;Yimin Zhu;Jianan Yang;Lixian Zhang;Ming Liu;
      Pages: 5302 - 5313
      Abstract: This article is devoted to the investigation of target tracking control of quadrotor with intermittent measurements, i.e., the measurements of target position are not continuously available. A stochastic switched systems approach is utilized to formulate such a tracking process of quadrotor by splitting it into two modes according to whether the measurements are available. The mode switching of the underlying system is subject to a Markov process to account for the inherent nonmemory randomness of the dwell time of each mode, in contrast with the existing studies that impractically require the duration of the measurements to be greater than a given constant. The stability criteria for the underlying system are derived, upon which a switched controller is designed for the quadrotor position control, rather than the determination of the bounds of the dwell time for given predesigned controllers in each mode. Simulations are presented to demonstrate the effectiveness of the developed theoretical results and the superiority of the designed switched controller for tracking control of quadrotor with intermittent target measurements.
      PubDate: TUE, 07 MAR 2023 10:13:24 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Nonlinear Control Allocation Using a Piecewise Multilinear Representation

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      Authors: Jahanzeb Rajput;Hafiz Zeeshan Iqbal Khan;
      Pages: 5314 - 5323
      Abstract: Nonlinear control allocation is an important part of modern nonlinear dynamic inversion based flight control systems, which require a highly accurate model of aircraft aerodynamics. Generally, an accurately implemented onboard model determines how well the system nonlinearities can be canceled. Thus, a more accurate model results in better cancelation, leading to higher performance of the controller. In this article, a control system is presented that combines nonlinear dynamic inversion with a piecewise multilinear representation based control allocation. The piecewise multilinear representation is developed through a generalization of the Kronecker product for block matrices, combined with the canonical piecewise linear representation of nonlinear functions. Analytical expressions for the Jacobian of the piecewise multilinear model are also presented. The proposed formulation gives an equivalent analytical representation of piecewise multilinear aerodynamic data and, thus, is capable of accurately modeling nonlinear aerodynamics over the entire flight envelope of an aircraft. The resulting nonlinear controller is applied to control a tailless flying wing aircraft with ten independently operating control surfaces. The simulation results for two innovative control surface configurations indicate that an accurate control allocation performance can be achieved, leading to better tracking performance compared with the control allocation methods based on multivariate polynomials and splines.
      PubDate: TUE, 14 MAR 2023 10:02:19 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Reducing Bias for Multistatic Localization of a Moving Object by
           Transmitter at Unknown Position

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      Authors: Jian Pei;Gang Wang;Dominic K. C. Ho;Lei Huang;
      Pages: 5324 - 5341
      Abstract: Multistatic localization plays an important role in object localization. In this article, we address the problem of multistatic localization of a moving object in the absence of transmitter position when the transmitter is not synchronized with the receivers. We propose to jointly estimate the unknowns, including the object position and velocity, the transmitter position, and the clock and frequency offsets, using both time delay and Doppler frequency shift measurements. To this end, we first formulate a constrained weighted least squares (CWLS) minimization problem, whose solution is found to have significant bias caused by approximations in transforming the model equations and solving the problem. To reduce the bias, we further formulate a nonconvex bias reduced CWLS (BR-CWLS) problem by imposing a quadratic constraint, which is constructed by considering the second-order noise and errors of the matrices and vectors involved in the CWLS problem. One particular aspect of the proposed BR-CWLS method is that the errors in approximating the weighting matrix are taken into consideration for bias reduction. The nonconvex BR-CWLS problem is solved by applying the semidefinite relaxation technique to relax it as a convex semidefinite program. In addition, we show through the mean square error (MSE) analysis that the performance of the BR-CWLS solution can approach the Cramer–Rao lower bound performance when the noise is not significant. We also derive the theoretical bias expression for evaluating the amount of bias. Simulation results demonstrate the good performance of the proposed method in terms of both MSE and bias.
      PubDate: WED, 15 MAR 2023 10:01:29 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Distributed Detection With Generalized Locally Most Powerful Fusion of
           Compressed Local Multiframe Test Statistics

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      Authors: Jing Lu;Shenghua Zhou;Xiaojun Peng;
      Pages: 5342 - 5362
      Abstract: Distributed radar multiframe detection (DR-MFD) can detect dim targets through a longer fusion period of multiradar observations. However, the communication cost associated with DR-MFD is usually huge, causing a heavy hardware burden in the real application. This article investigates the dim target detection problem with the DR-MFD system and aims to realize the DR-MFD with a good detection performance and a low communication cost. To compress local data, local test statistics formed at each frame are quantized by the Fisher information maximization-based quantization (FIMQ), and the quantized outputs are transmitted to a fusion center (FC). A generalized locally most powerful (GLMP) detector is derived to fuse the received multiframe quantized outputs at the FC. It is proved that the FIMQ is asymptotically optimal for the GLMP detector in the sense of detection performance. A closed-form solution is provided to find the local quantization thresholds of the FIMQ. Simulation results indicate that the GLMP detector outperforms the generalized likelihood ratio detector in most situations. The asymptotic optimality of the FIMQ is also corroborated. In addition, for the GLMP detector with 3-bit FIMQ, the signal-to-noise ratio loss caused by data quantization can reach a 0.11 dB level.
      PubDate: THU, 16 MAR 2023 10:01:29 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Use of Stepped Carrier Frequency in Fast Scan Mode to Detect Small Targets
           on the Sea Surface

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      Authors: Inoh Choi;Sangbin Cha;Kyungtae Kim;Sanghong Park;
      Pages: 5363 - 5377
      Abstract: To detect a small target on the sea surface, we propose a detection algorithm that operates in three steps: 1) representation of a 3-D complex cube in the (radial range, pulse time, and scan time) domain by using stepped carrier frequency in fast scan mode; 2) intrascan integration to transform complex cube from the pulse time domain to the radial velocity domain by using Fourier transform; and 3) interscan integration to detect a small target by using a conventional optimal coherent detector with a Fourier relationship between stepped carrier frequency and initial radial range, and a discriminator between a true target and false targets. The proposed method improves the ability to detect a small target and reduces the sensitivity to sea clutter despite relatively low radial range resolution. In experiments, our proposed method could perform accurate and robust detection of a small target.
      PubDate: TUE, 14 MAR 2023 10:02:19 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • End-to-End Monocular Pose Estimation for Uncooperative Spacecraft Based on
           Direct Regression Network

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      Authors: Haoran Huang;Bin Song;Gaopeng Zhao;Yuming Bo;
      Pages: 5378 - 5389
      Abstract: Deep learning shows good performance in monocular pose estimation and has been used by some space researchers to solve the monocular pose estimation problem of uncooperative spacecraft. However, existing deep-learning-based methods are mostly trained with keypoint regression errors unnecessarily reflecting actual pose errors, limiting their learning performance. In this article, an end-to-end pose estimation network based on the convolutional neural network is proposed for the uncooperative spacecraft. First, we design a keypoint regression subnetwork based on the multibranch structure to regress the two-dimensional (2-D) keypoint locations. Then, we propose a pose estimation subnetwork to estimate the pose of the target spacecraft from the predicted 2-D keypoints and the corresponding 3-D keypoints of the target model, which allows the end-to-end training of the overall pose estimation network with actual pose error. The experimental results on two public datasets demonstrate that the proposed method can accurately estimate the target spacecraft pose in the presence of scale variance and dynamic Earth background and has better pose estimation accuracy than the current state-of-the-art methods. In addition, the proposed method shows good generalization performance and near real-time efficiency.
      PubDate: TUE, 14 MAR 2023 10:02:19 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Unified NOMA Framework in Beam-Hopping Satellite Communication Systems

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      Authors: Xuyang Zhang;Xinwei Yue;Tian Li;Zhihao Han;Yafei Wang;Yong Ding;Rongke Liu;
      Pages: 5390 - 5404
      Abstract: This article investigates the application of a unified nonorthogonal multiple access framework in beam hopping (U-NOMA-BH)-based satellite communication systems. More specifically, the proposed U-NOMA-BH framework can be applied to code-domain NOMA-based BH (CD-NOMA-BH) and power-domain NOMA-based BH (PD-NOMA-BH) systems. To satisfy dynamic-uneven traffic demands, we formulate the optimization problem to minimize the square of discrete difference by jointly optimizing power allocation, carrier assignment, and beam scheduling. The nonconvexity of the objective function and the constraint condition is solved through Dinkelbach's transform and variable relaxation. As a further development, the closed-form and asymptotic expressions of outage probability are derived for the CD/PD-NOMA-BH systems. Based on approximated results, the diversity orders of a pair of users are obtained in detail. In addition, the system throughput of the U-NOMA-BH is discussed in a delay-limited transmission mode. Numerical results verify that: 1) the gap between traffic requests of the CD/PD-NOMA-BH systems appears to be more closely compared with orthogonal multiple access-based BH (OMA-BH); 2) the CD-NOMA-BH system is capable of providing the enhanced traffic request and capacity provision; and 3) the outage behaviors of the CD/PD-NOMA-BH are better than that of OMA-BH.
      PubDate: TUE, 21 MAR 2023 10:03:05 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Singularity-Avoidance Prescribed Performance Control for Spacecraft
           Attitude Tracking

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      Authors: Jiakun Lei;Tao Meng;Weijia Wang;Heng Li;Zhonghe Jin;
      Pages: 5405 - 5421
      Abstract: The attitude tracking control problem with preassigned performance requirements has earned tremendous interest in recent years, and the prescribed performance control (PPC) scheme is often adopted to tackle this problem. Nevertheless, traditional PPC schemes may suffer singularity problems when the expected state constraint is violated. This may cause a decrease in the control effect or even instability of the system. Motivated by this issue, this article proposes a singularity-avoidance PPC scheme (SAPPC) to deal with these problems, of which the core is a shear-mapping-based error transformation procedure that globally transforms the original error state without singularity. Based on the presented nonsingular error transformation, this article presents a perspective to guarantee the satisfaction of given transient performance requirements by leading the state trajectory converging along a preassigned smooth trajectory called reference performance function (RPF). Based on the concept of the RPF, this article naturally adopts an error-defined-type state constraint, providing a time-varying constraint boundary and alleviating the potential overcontrol problem at the steady state. Furthermore, in order to validate the proposed SAPPC scheme, a simple backstepping controller is developed by employing the predefined-time stability technique and the dynamic surface control technique. Finally, theoretical analysis and numerical simulation results are presented to validate the proposed control scheme's effectiveness and robustness.
      PubDate: MON, 20 MAR 2023 10:06:56 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Explicit Joint Resolution Limit for Range and Direction-of-Arrival
           Estimation in MIMO Radar

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      Authors: Han Zhang;Dazhuan Xu;Nan Wang;
      Pages: 5422 - 5432
      Abstract: Resolution capability is an important metric for radar parameter estimation. However, the resolution limit for multiparameter estimation has not been adequately studied. In this article, we exploit results from Shannon's information theory to derive a joint resolution limit (JRL) for range and direction-of-arrival (DOA) estimation in multiple-input multiple-output radar. Based on the general model, the closed-form expression of scattering information is obtained for two targets with complex Gaussian scattering properties. A critical state is considered where the quadrature component of the scattering information is 1 bit, and corresponding intervals of range and DOA are defined as the JRL. Under the assumption of closely spaced targets, the approximated explicit JRL is derived by a second-order Taylor series, which indicates that the JRL is inversely proportional to the number of array elements, the square root of the signal-to-noise ratio, and the signal bandwidth to the carrier frequency ratio. In addition, closed-form approximations for a range resolution limit and a DOA resolution limit are obtained as special cases. It is indicated that these resolution limits are general and not based on any specific resolution method. The accuracy and the validity of the proposed resolution limits are verified by numerical simulations.
      PubDate: THU, 23 MAR 2023 10:02:16 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Pursuer Aim Identification for Evaders Using Bearing-Only Measurements

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      Authors: Songzhou Li;Di Zhou;Yutang Li;Runle Du;Jiaqi Liu;
      Pages: 5433 - 5447
      Abstract: Identifying the aim of a pursuer quickly and accurately is vital to taking appropriate measures and successfully avoiding capture. In order to determine whether an evader in a flying formation is aimed by the pursuer or not, an aim identification algorithm is proposed based on the stationarity difference of the mixed product of three direction vectors: 1) the evader's velocity vector; 2) the evader-to-pursuer line-of-sight (LOS) vector; and 3) at the same instant another such LOS vector at a subsequent instant. Due to the effect of the guidance law, the subsequent LOS always shows a tendency to keep coplanar with the velocity of the aim and the previous LOS such that the mixed product obtained by the aim changes very slowly. In other words, the mixed product sequence obtained by the aim based on the known velocity and the bearing measurements of the LOS has relatively good stationarity, while the stationarity of the sequence obtained by the evader not being aimed at is poor. Based on the stationarity difference, the mixed product sequences are used to formulate a chi-square distributed statistic such that the aim identification is equivalently transformed into a hypothesis testing problem. Simulation results are given to verify the effectiveness of the proposed algorithm.
      PubDate: MON, 20 MAR 2023 10:06:56 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Target Detection for Distributed MIMO Radar With Nonorthogonal Waveforms
           in Cluttered Environments

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      Authors: Cengcang Zeng;Fangzhou Wang;Hongbin Li;Mark A. Govoni;
      Pages: 5448 - 5459
      Abstract: Orthogonal radar waveforms originating from spatially distributed transmitters usually arrive at a receiver in nonorthogonal forms, as they propagate through different paths with distinct delays and Doppler frequencies in distributed multi-input multi-output (MIMO) radar. Nonorthogonal waveforms complicate the composition of the target and clutter returns, making it more challenging to separate one from the other. In this article, we consider joint target detection and clutter mitigation in distributed MIMO radar. We first present a general signal model for distributed MIMO radar in cluttered environments. Next, we propose three families of detection solutions, including noncoherent detectors that require no phase estimation and are relatively simple to implement, coherent detectors that offer enhanced detection performance given accurate phase information, and hybrid detectors that are a compromise of the former two, requiring only local phase coherence but no explicit phase estimation. In addition, approximate solutions are proposed in each category with further complexity reduction, using high clutter-to-noise approximation or convex relaxation. Simulation results are presented to demonstrate the performance of the proposed detectors, which outperform their earlier counterparts that neglect the presence of clutter.
      PubDate: MON, 27 MAR 2023 10:07:10 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Interval Uncertainty-Oriented Optimal Control Method for Spacecraft
           Attitude Control

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      Authors: Chen Yang;Yuanqing Xia;
      Pages: 5460 - 5471
      Abstract: Research on uncertainty-oriented optimal attitude control of spacecraft with complex space environments and multisource uncertainties is a research hotspot. Considering that the uncertain parameters in the control system are difficult to quantify, this article proposed an interval uncertainty-oriented optimal control method based on the linear quadratic regulator (LQR) for spacecraft attitude control. The interval state-space equation of the spacecraft attitude dynamic with uncertain controlled feedback gain was constituted by expanding the deterministic model into an order-extended interval matrix format. Based on the interval uncertainty propagation method, the interval-based Riccati equation in LQR was proposed using the modified interval estimation method. Therefore, the interval-controlled feedback gain and interval cost function could be obtained, and the overestimation attributed to the interval expansion could be avoided. The interval-based reliability was investigated using the state–threshold interference model, and the interval-based safety index was developed. The interval uncertainty-based multiobjective optimal control model with constraints was proposed to balance both minimizations of the optimal control cost function and state vector fluctuation by considering these two interval indices as the constraints in optimal control. A flowchart and a numerical example of satellite attitude control were applied to reflect the effectiveness.
      PubDate: MON, 20 MAR 2023 10:06:56 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Coarray Tensor Completion for DOA Estimation

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      Authors: Hang Zheng;Zhiguo Shi;Chengwei Zhou;André L. F. de Almeida;
      Pages: 5472 - 5486
      Abstract: Sparse array direction-of-arrival (DOA) estimation using tensor model has been developed to handle multidimensional sub-Nyquist sampled signals. Furthermore, augmented virtual arrays can be derived for Nyquist-matched coarray tensor processing. However, the partially augmentable sparse array corresponds to a discontinuous virtual array, whereas the existing methods can only utilize its continuous part. Conventional virtual linear array interpolation techniques complete coarray covariance matrices with dispersed missing elements, but fail to complete the coarray tensor with whole missing slices. In this article, we propose a coarray tensor completion algorithm for 2-D DOA estimation, where the coarray tensor statistics can be entirely exploited. In particular, in order to impose an effective low-rank regularization on the slice-missing coarray tensor, we propose shift dimensional augmenting and coarray tensor reshaping approaches to reformulate a structured coarray tensor with sufficiently dispersed missing elements. Furthermore, the shape of the reformulated coarray tensor is optimized by maximizing the dispersion-to-percentage ratio of missing elements. As such, a coarray tensor nuclear norm minimization problem can be designed to optimize the completed coarray tensor corresponding to a filled virtual array, based on which the closed-form DOA estimation is achieved. Meanwhile, the global convergence of the coarray tensor completion is theoretically proved. Simulation results demonstrate the effectiveness of the proposed algorithm compared with other matrix-based and tensor-based methods.
      PubDate: THU, 30 MAR 2023 10:01:47 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Robust Nonlinear Filtering: A Non-Bayesian Approach

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      Authors: Gunner S. Fritsch;Kyle J. Demars;
      Pages: 5487 - 5502
      Abstract: Today, the field of practical estimation is dominated by linear-Bayesian estimators as these familiar techniques are computationally efficient and can be quickly tailored to many systems of interest. However, beyond the common weaknesses of linear estimators to non-Gaussian noises and nonlinear transformations, Bayes' rule itself lacks robustness, as it inherently assumes that all statistical models are exactly known. Therefore, in lieu of filters outfitted with linear-Bayesian updates, this work proposes a method to derive nonlinear, non-Bayesian updates from first principles using the theory of generalized variational inference (GVI)—an optimization approach to information fusion. By selecting different divergence measures, loss functions, and feasible distributions, a wide variety of robust/conservative filter updates are quickly built and tested using numerical optimization. From these GVI candidates, a suitable update that uses the robust $\gamma$-loss function is selected based upon its desirable estimation behavior. Via analytical optimization and Gaussian mixture modeling, the $\gamma$-loss GVI update is realized as a nonlinear, closed-form filter that is well-suited for practical estimation.
      PubDate: TUE, 04 APR 2023 10:03:56 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Data-Compatibility Analysis Based on Expectation-Maximization Algorithm
           With Unknown Noise Statistics

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      Authors: Junyi Zuo;Shi Qiu;Wenzheng Wang;
      Pages: 5503 - 5515
      Abstract: In this article, data-compatibility analysis (DCA) problem is investigated with both the measurement noise and input noise considered. The expectation-maximization (EM) algorithm is exploited to deal with the unknown noise statistics. Motivated by the fact that although there exists an excellent EM based estimator for dynamic systems, it cannot be used in the context of DCA due to the presence of nonadditive input noises, a generalized EM based estimator is developed to reconstruct the flight path, as well as to estimate the sensor model parameters. Making full use of the special dependencies among the state components and the fact that the associated dynamic system has an affine nonlinear structure, the generalized estimator retains many advantages of the existing EM based one, such as the simple optimization process for the unknowns. Experimental results show that it can give more accurate results than the classical output error method and the existing EM based one in the case of high input noise.
      PubDate: TUE, 21 MAR 2023 10:03:05 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Fault-Tolerant Coverage Control of Multiple Satellites: A Differential
           Graphical Game Approach

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      Authors: Yuan Ni;Hao Yang;Bin Jiang;
      Pages: 5516 - 5529
      Abstract: This article considers the regional coverage problem of multiple satellites with full consideration of the coupled position–attitude characteristics, the optimality, and the cooperation among satellites. In the fault-free case, a configuration condition of satellite formation flying (SFF) is established under which the desired coverage configuration is designed. In the presence of completely failed satellites, a configuration adjustment scheme is provided such that the coverage tasks of failed satellites are handed over to other healthy ones. Based on the proposed configurations, a distributed near-optimal coverage controller that achieves the optimal coverage control goal is designed for each satellite by utilizing the differential graphical game theory and the approximate dynamic programming (ADP) method. Simulation results show the effectiveness of the proposed methods.
      PubDate: THU, 23 MAR 2023 10:02:16 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Enhanced Spatial Spoofing Detection With and Without Direction of Arrival
           Estimation

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      Authors: Johannes Rossouw van der Merwe;Alexander Rügamer;Melanie Lipka;
      Pages: 5530 - 5540
      Abstract: Spoofing, the transmission of false global navigation satellite system signals, threatens receivers. The first step in dealing with spoofers is to detect the attack. Once a spoofing attack is detected, spatial detection methods using an array of antennas can additionally mitigate spoofing through spatial nulling. This article presents a spatial spoofing detection approach using the estimated antenna steering vector (ASV) from direction of arrival (DOA) estimation. Furthermore, the method is compared with blind detection and post-DOA estimation methods. Simulation results show that the approach is comparable to the current state-of-the-art if the same a priori information is used. However, superior performance is achieved if the attitude of the antenna array is known. The presented approach represents an alternative method for spatial spoofing detection that leverages the DOA estimates but remains in the ASV domain. Therefore, it benefits from noise modeling and ambiguity resolution.
      PubDate: TUE, 28 MAR 2023 10:02:38 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Aggressive Formation Tracking for Multiple Quadrotors Without Velocity
           Measurements Over Directed Topologies

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      Authors: Jie Lin;Zhiqiang Miao;Yaonan Wang;Guoqiang Hu;Rafael Fierro;
      Pages: 5541 - 5553
      Abstract: This article addresses the aggressive formation tracking problem of quadrotors without velocity measurements over directed interaction topologies. A distributed formation control scheme is proposed for a multiple-quadrotor system, which includes an outer-loop position controller to achieve the desired formation and an inner-loop attitude controller to regulate the attitudes. First, a second-order auxiliary system is designed to overcome the unmeasurable linear velocity. Next, the position controller is presented using only the position information under the directed topologies. Then, the geometric attitude controller is developed to carry out aggressive rotation maneuvers. Furthermore, an auxiliary system based on Lie algebra is proposed to eliminate the unreliable angular velocity feedback caused by disturbances and bias perturbations. Using Lyapunov stability theory on a cascaded system, it is rigorously proven that the proposed formation controller is asymptotically stable. Finally, the applicability and performance of the proposed control scheme are demonstrated by the numerical simulations.
      PubDate: WED, 22 MAR 2023 10:02:20 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Online Path Decision of No-Fly Zones Avoidance for Hypersonic Vehicles
           Based on a Graph Attention Network

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      Authors: Yuan Zhang;Ran Zhang;Huifeng Li;
      Pages: 5554 - 5567
      Abstract: The path decision problem for a hypersonic vehicle has been recently formulated as it helps to determine a good initial guess for trajectory optimization with no-fly zone constraints. This path decision problem is a hybrid problem. Discrete variables optimize paths and continuous variables optimize dynamics, which consumes costly computation time and is hard to apply in online scenarios. To reduce the computation, instead of directly solving the hybrid problem, we design a heuristics approach taking advantage of the interpretability and flexibility of a graph attention network (GAT). The path decision is modeled by a directed graph and transformed into a GAT training problem, and the resulting GAT can directly output a path in online use. During this work, there are two innovations: 1) GAT customization and 2) offline training. First, we define the mask to express the graph structure, and model the problem-specific decoder process in the GAT, thus meeting the path decision logic of no-fly zones avoidance and ensuring the solutions are feasible. Second, we numerically integrate dynamics by a path-following guidance law, calculate the total control effort as the cost function, and use this cost to train the GAT based on the widely used REINFORCE, thus conforming to dynamics that are practical for hypersonic vehicles. Simulation results illustrate the high-accuracy path decision, much faster calculation, and generalization on no-fly zone layouts and numbers.
      PubDate: TUE, 21 MAR 2023 10:03:05 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Exploiting Unit Circle Roots for Improved Radar Moving Target Detection in
           Low Rank Clutter With Limited Secondary Data

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      Authors: Jared Smith;Arnab Shaw;
      Pages: 5568 - 5587
      Abstract: Measured radar clutter often exhibits a low-rank structure that can be exploited for designing adaptive moving target detectors to reduce the amount of secondary data necessary for desirable detection performance. It has been shown recently that for symmetrically spaced pulse trains significant performance improvements can be achieved by constraining the roots of the adaptive matched filter (AMF) polynomial onto the unit circle (UC) that satisfies a theoretical property of the known-covariance AMF. In this article, the UC-roots approach is extended to develop a generalized likelihood ratio test (GLRT) for radar moving target detection in low-rank clutter with limited secondary data. The proposed unit circle GLRT (UC-GLRT) uses the properties of orthogonal projection matrices to enforce the UC roots constraint. The asymptotic performance analysis of the proposed UC-GLRT conducted in this article is verified by simulation. Simulation studies with limited secondary data demonstrate the superior performance of the UC-GLRT approach over several existing detectors. Clutter rank and target velocity mismatch issues are also studied.
      PubDate: TUE, 28 MAR 2023 10:02:38 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Adaptive Antisaturation Prescribed-Time Control for Payload Retrieval of
           Tethered Space System

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      Authors: Gefei Shi;Zheng H. Zhu;
      Pages: 5588 - 5596
      Abstract: This article presents an adaptive antisaturation prescribed-time control (AAPTC) scheme for the stable payload retrieval of a two-body tethered space system following the desired libration state. The tethered payload is retrieved at the analytical speed function corresponding to the desired stable libration state. At the same time, the thrust at the payload is applied with the proposed AAPTC scheme to closely track the desired state to eliminate any deviation from the desired libration state due to external disturbances. The influence of the thrust saturation is minimized by a neural dynamics-based adaptive antisaturation compensator (ASC) that quickly drives the control system away from the saturated region. The Lyapunov stability of the proposed control scheme is proved analytically. Numerical simulations validate the effectiveness of the proposed prescribed-time strategy and the adaptive ASC.
      PubDate: FRI, 24 MAR 2023 10:02:18 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Platform Errors Estimation Method Based on Radar Echo for Spaceborne
           Doppler Scatterometer

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      Authors: Xiaonan Yao;Xiaoqing Wang;Lixia Liu;Shubo Liu;Xiang Su;Haifeng Huang;
      Pages: 5597 - 5612
      Abstract: The Doppler scatterometer is a type of rotating radar platform used for the estimation and monitoring of ocean current to achieve fast mapping of global vector current with a monostatic system. Accurate measurement of the ocean surface current imposes strong requirements on the accuracy of the platform errors. If the ocean surface current speed measurement accuracy can be less than 5 cm/s, the slant range error should be smaller than 20 m and the attitude measurement error should reach 1 × 10−3°. No available attitude determination technique is capable of this level of accuracy. In this study, we propose a method of platform errors estimation based on echo signals. A model describing the relation of the Doppler centroid to platform errors is established. Then, a Doppler centroid frequency estimation method based on deramping is proposed to improve the estimation accuracy, which showed a 26.15% improvement compared with the classical average cross-correlation coefficient method. Finally, using the weighted least square technique, the platform errors estimation method based on Doppler centroid estimation of echo signals from the stationary land area is constructed. Simulation experiments were performed to analyze the accuracy, feasibility, and robustness of the proposed method. Through simulation of one circle echo data, the root-mean-square errors of slant range error, roll, pitch, and yaw were shown to reach 1.12 × 10−1 m, 1.69 × 10−6°, 2.44 × 10−6°, and 8.52 × 10-7°, respectively. These findings represent a much smaller error level than the requirement for the slant range error and attitude angles.
      PubDate: FRI, 24 MAR 2023 10:02:18 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Global Approach to the Design of Insulation Systems for Aerospace
           Electrical Assets: Focus on Printed Circuit Board

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      Authors: Qichen Yang;Gian Carlo Montanari;Debasish Nath;
      Pages: 5613 - 5623
      Abstract: The evolution of power electronics in aerospace and, in general, electrified transportation assets, is centered on the development of MV power electronics converters, which maximize power density and efficiency. This turns into a high-field and temperature design, ultrafast switching, and high-frequency modulation. Such conditions may pose significant and unpreceded electrothermal stresses on insulation systems of electrical asset components, such as printed circuit boards (PCBs). The magnitude, profile, and time behavior of stresses, as well as their effect on intrinsic and extrinsic aging mechanisms, have to be known, being the basis of a highly reliable electrothermal insulation design able to provide a specified life at specified failure probability. This article introduces an innovative criterion to optimize the design of the insulation system of MV PCB, according to the new “three-leg” approach that consists of interlacing electric field simulation, life and discharge modeling, and partial discharge testing. The essence of such an approach is to design an insulation system, as regards bulk and surface subcomponents, by a global view, where aging processes rule design rather than macroscopic failure risk. Accordingly, life and reliability are determined by appropriate models, accounting for intrinsic aging, and, in addition, the design ensures that operation stresses do not trigger extrinsic accelerated aging mechanisms, as those associated with partial discharges. It is shown in this article that the proposed approach can lead to an innovative and effective design, delivering the specified life and ensuring that the electrical field on the surface (and in the bulk) is limited to values below the threshold for partial discharge inception. This can question, in principle, the use of creepage and clearance tools, since the new design procedure will be related to the minimization of the likelihood of partial discharge inception, rather than to avoiding the risk of macroscopic surface discharges.
      PubDate: THU, 30 MAR 2023 10:01:47 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Effects of Temperature and Pressure on Failure Risk of Electric Motors
           Based on Partial Discharge Measurements

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      Authors: Waqar Hassan;Ghulam Amjad Hussain;Farhan Mahmood;Muhammad Shafiq;Gian Carlo Montanari;
      Pages: 5624 - 5633
      Abstract: Typically, partial discharge (PD) diagnostic tests are carried out in laboratories under standard atmospheric conditions. However, during the operation of electric motors (EMs), environmental stresses may increase due to variations in the atmospheric pressure and temperature. This is, for example, the case of electric aircraft and, in general, other aerospace applications. Experimental results show that these stresses may affect the level and phenomenology of PD, thereby compromising the reliability of insulation system. Therefore, neglecting the actual environmental stresses involved in PD testing or monitoring may result in an incorrect interpretation of the health condition of the EMs. This study presents a framework to properly assess the impact of environmental stresses on PD activity and estimate the failure risk of EM insulation. For this purpose, PD diagnostics were carried out by performing a number of experiments on seven EMs in a climate-controlled chamber. PD characteristic features at different levels of environmental stresses are extracted from measurements in order to investigate the PD severity and infer their effect on the resulting insulation degradation. Also, various probability distribution functions are used to estimate the probability of failure and risk assessment corresponding to the PD severity at different levels of environmental stresses. The proposed risk assessment technique can be helpful to plan regular maintenance activities and repair–replacement actions, keeping the reliability at the level requested for most industrial applications involving variable temperature and pressure operations.
      PubDate: TUE, 28 MAR 2023 10:02:38 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • URGLQ: An Efficient Covariance Matrix Reconstruction Method for Robust
           Adaptive Beamforming

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      Authors: Tao Luo;Peng Chen;Zhenxin Cao;Le Zheng;Zongxin Wang;
      Pages: 5634 - 5645
      Abstract: The computational complexity of the conventional adaptive beamformer is relatively large, and the performance degrades significantly due to the model mismatch errors and the unwanted signals in received data. In this article, an efficient unwanted signal removal and Gauss–Legendre quadrature-based covariance matrix reconstruction method is proposed. Different from the prior covariance matrix reconstruction methods, a projection matrix is constructed to remove the unwanted signal from the received data, which improves the reconstruction accuracy of the covariance matrix. Considering that the computational complexity of most matrix reconstruction algorithms is relatively large due to the integral operation, we proposed a Gauss–Legendre quadrature-based method to approximate the integral operation while maintaining accuracy. Moreover, to improve the robustness of the beamformer, the mismatch in the desired steering vector is corrected by maximizing the output power of the beamformer under a constraint that the corrected steering vector cannot converge to any interference steering vector. Simulation results and prototype experiments demonstrate that the performance of the proposed beamformer outperforms the compared methods and is much closer to the optimal beamformer in different scenarios.
      PubDate: THU, 30 MAR 2023 10:01:47 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Tracking Algorithm for Debris Cloud Fragments Produced by Different
           Hypervelocity Impacts Through Image Processing

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      Authors: Xin Tian;Xiao Wang;Yan Song;Guoliang Wei;Ru Zeng;
      Pages: 5646 - 5657
      Abstract: The amount of man-made debris is sharply increasing because of the increase in human activities being carried out in space. This could seriously threaten spacecraft performance and even lead to system instability. In order to guarantee the safe operation of spacecraft, it is necessary to make an in-depth investigation on debris clouds produced by different hypervelocity impact (HVI) experiments and make an accurate damage estimation based on debris characteristics. The interest of this article is to discuss the main properties of debris clouds that play a central role in damage estimation of the rear wall of the Whipple shield protective structure. Those properties include the impact velocity, the evolution velocity, the diameter of the projectile, and the incident angle of debris to the rear wall. The purpose of this article is to track debris fragments and construct a simple trajectory model by employing 2-D image processing techniques, and make a damage estimation of debris clouds through different experimental results comparison. More specifically, by using the mean shift algorithm, image noise is greatly eliminated while reserving edges. Then, in terms of the $K$-means algorithm, the overlapping that occurs in some frames is mostly handled and fragments of the underlying images are successfully segmented. Moreover, by utilizing the scale invariant feature transform technique, the image matching is fulfilled, and the trajectory is further established. Finally, based on the established trajectory, the comparison of debris clouds generated by different HVI experiments is provided, through which an accurate damage estimation is formulated. Experimental results demonstrate the effectiveness and validity of the proposed algorithm via image processing.
      PubDate: WED, 05 APR 2023 10:04:46 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Deep Learning-Based Target Pose Estimation Using LiDAR Measurements in
           Active Debris Removal Operations

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      Authors: Enrique Aldao Pensado;Luis Miguel González de Santos;Higinio González Jorge;Manuel Sanjurjo-Rivo;
      Pages: 5658 - 5670
      Abstract: In this article, a study on the use of a commercial global-flash LiDAR sensor in active debris removal operations is presented. This type of activity requires precise knowledge of the position and orientation of the target to be removed. For these missions, relative navigation devices, such as cameras or LiDAR sensors are typically regarded. In this study, the mission profile defined in the e.Deorbit System Requirements Review was considered, and data acquisition and processing from a commercial ASC GSFL-16KS LiDAR sensor were simulated. As the main novelty of this work, the use of multilayer perceptron neural networks for the processing of LiDAR depth images is proposed in order to obtain an estimate of the pose of the target. Using the results of the neural networks, an iterative closest point algorithm is applied to refine the calculation of the pose. The accuracy and computation time of the system were evaluated, obtaining robust and computationally efficient results in the proposed study cases.
      PubDate: TUE, 28 MAR 2023 10:02:38 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A STAP Detection Scheme for Low Sample Support Maritime Environments

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      Authors: Malcolm Wong;Elias Aboutanios;Luke Rosenberg;
      Pages: 5671 - 5683
      Abstract: In airborne radar, reduced rank space-time adaptive processing techniques are employed when non-stationarity and non-homogeneity of the clutter causes insufficient sample support. There have been many approaches proposed to address this problem, including principal components, the cross-spectral metric, and the multistage Wiener filter. This latter approach is superior to other reduced rank techniques in terms of computational efficiency, sample support, and rank requirements. Regarding operation in heterogeneous environments, the single dataset approach for clutter suppression has been proposed and operates solely on the cell under test to obtain a clutter covariance estimate. It is, therefore, highly effective in environments with limited training data that are homogeneous with the test data. In this article, a single dataset detection approach under the framework of the multistage Wiener filter is proposed and analyzed to enhance clutter suppression capabilities. The target detection performance of the filter is evaluated using simulated maritime radar data.
      PubDate: TUE, 18 APR 2023 10:02:30 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Neural Network-Based Multitarget Detection Within Correlated Heavy-Tailed
           Clutter

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      Authors: Stefan Feintuch;Haim H. Permuter;Igal Bilik;Joseph Tabrikian;
      Pages: 5684 - 5698
      Abstract: This work addresses the problem of range–Doppler multiple target detection in a radar system in the presence of slow-time correlated and heavy-tailed distributed clutter. Conventional target detection algorithms assume Gaussian-distributed clutter, but their performance is significantly degraded in the presence of correlated heavy-tailed distributed clutter. Derivation of optimal detection algorithms with heavy-tailed distributed clutter is analytically intractable. Furthermore, the clutter distribution is frequently unknown. This work proposes a deep learning-based approach for multiple target detection in the range–Doppler domain. The proposed approach is based on a unified neural network (NN) model to process the time-domain radar signal for a variety of signal-to-clutter-plus-noise ratios (SCNRs) and clutter distributions, simplifying the detector architecture and the NN training procedure. The performance of the proposed approach is evaluated in various experiments using recorded radar echoes, and via simulations, it is shown that the proposed method outperforms the conventional cell-averaging constant false-alarm rate (CFAR), the trimmed-mean CFAR, and the adaptive normalized matched-filter detectors in terms of probability of detection in the majority of tested SCNRs and clutter scenarios.
      PubDate: WED, 05 APR 2023 10:04:46 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Commercial Fixed-Wing Drone Redirection System Using GNSS Deception

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      Authors: Myoung-Ho Chae;Seong-Ook Park;Seung-Ho Choi;Chae-Taek Choi;
      Pages: 5699 - 5713
      Abstract: As drones become more common today, the threat of reconnaissance or attack drones to core facilities has increased, and countermeasures against them have become essential. In this study, a drone redirection system was proposed to counter the illegal intrusion of commercial fixed-wing drones. The drone redirection system was designed as a closed-loop system that automatically redirects the drone to a target position. The main novelty of this study is a proposed system that can automatically redirect a commercial fixed-wing drone, which has not been previously explored. We proposed two strategies for redirecting drones. Additionally, simple drone modeling with a path-following algorithm was used to easily model various drones. The drone model was then tuned using flight test data, and the results were compared. Simulations were performed on the designed drone redirection system model to verify the performance of the two proposed strategies for redirecting drones in conjunction with drone fail detection and innovation check. The performance of the drone redirection system was assessed through flight tests of Remo-M and simulations of MicroPilot's Hardware-in-the-Loop Simulator (HWILS). Through simple drone modeling, drone flight tests, and the test results from HWILS, it was proved that the drone redirection system can be applied to various fixed-wing drones.
      PubDate: MON, 03 APR 2023 10:05:16 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Array Scheduling With Power and Bandwidth Allocation for Simultaneous
           Multibeam Tracking Low-Angle Targets in a VHF-MIMO Radar

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      Authors: Haowei Zhang;Junkun Yan;Weijian Liu;Qun Zhang;
      Pages: 5714 - 5730
      Abstract: The very high frequency (VHF) radar suffers from the multipath interference and false alarms when tracking low-angle targets. The colocated multiple-input multiple-output (MIMO) radar technique can mitigate these adverse effects with waveform diversity. However, the limited resources restrict the potential of the VHF-MIMO radar. In this article, an array scheduling with power and bandwidth allocation (ASPBA) strategy is proposed for the VHF-MIMO radar tracking low-angle targets. The predicted posterior Cramér-Rao lower bound (PCRLB) under the multipath interference and measurement origin uncertainty effects is derived to serve as the optimization metric. The measurement amplitude information is incorporated into the PCRLB to facilitate target detection and tracking. It is shown that the ASPBA problem is a mixed integer programming and NP-hard problem, where the array, power, and bandwidth allocation variables are both coupled in the objective and constraints. An efficient three-stage-based solution is proposed for problem-solving. The approximated relationship among three variables is derived using Hölder's inequality, and an auxiliary variable is introduced to describe the array contribution. Thereby, the array scheduling is determined by a heuristic rounding algorithm, and the power and bandwidth allocation are achieved using the approximated relationship. Simulation results confirm the effectiveness and efficiency of the proposed ASPBA strategy, compared with state-of-the-art algorithms. It is also shown that the target height is a main factor that influences the resource allocation results in the low-angle tracking scenario.
      PubDate: MON, 10 APR 2023 05:53:12 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Simultaneous Planning and Execution for Safe Flight of Quadrotors
           Suffering One Rotor Loss and Disturbance

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      Authors: Zhou Liu;Lilong Cai;
      Pages: 5731 - 5747
      Abstract: Safety is crucial for quadrotor flight, especially when the system suffers one rotor loss and disturbance. To enable quadrotors to survive in these worst cases, this article proposes a simultaneous planning and execution (SPAE) system strategy. First, the Euler angles under different equilibrium states are solved to ensure the feasibility of the control input when the quadrotors encounter rotor loss and disturbance. Based on the admissible Euler angle solutions, an emergency control strategy is proposed to realize desired angles by recalculating the desired trajectory and the disturbance observer. Meanwhile, this control strategy sacrifices the yaw angle control to handle further underactuation of the system caused by complete rotor failure. In addition to rotor losses, unsafe flight caused by actuator saturation is considered in the SPAE strategy, where the planning and execution are simultaneous. As a decision-maker, the planning considers the current disturbances and constraints of actuators, then generates a feasible trajectory for the controller execution. As an executor of the decision, the controller feedback estimated disturbance information to the trajectory planning, which can help trajectory planning make admissible decisions consistent with the current situation. As a result, the actuator saturation caused by the underestimation of disturbance can be avoided under a feasible regenerated trajectory. Extensive simulations and experiments prove the validity of the proposed SPAE strategy.
      PubDate: MON, 10 APR 2023 05:53:12 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Distributed Event-Triggered Nonlinear Fusion Estimation Under Resource
           Constraints

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      Authors: Rusheng Wang;Bo Chen;Zhongyao Hu;Li Yu;
      Pages: 5748 - 5767
      Abstract: This article studies the event-triggered distributed fusion estimation problems for a class of nonlinear networked multisensor fusion systems without noise statistical properties. In practice, the sensor-to-remote estimator channel and the smart sensor-to-fusion center channel of the communication network will be faced with some resource constraint problems. To meet the finite communication resources during the information transmission, an event-triggered strategy and a dimensionality reduction strategy are introduced in a unified network framework to reduce the communication traffic. Since the reduction of communication information will inevitably degenerate the estimation performance, two kinds of compensation strategies in terms of a unified model are proposed to restructure the untransmitted information. Then, the local/fusion estimators are designed based on the compensation information. Moreover, the linearization errors caused by the Taylor expansion are modeled by the state-dependent matrices with uncertain parameters when establishing estimation error systems, and then, different robust recursive optimization problems are established to determine the estimator gains and the fusion criteria. Meanwhile, the stability conditions are also presented such that the square errors of the designed nonlinear estimators are asymptotically bounded. Finally, a vehicle localization system is employed to demonstrate the effectiveness and advantages of the proposed methods.
      PubDate: WED, 12 APR 2023 10:02:02 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • L-Shape-Model-Based Vehicle Tracking With Joint Kinematic and Geometric
           Estimation Using Lidar

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      Authors: Dan Song;Ratnasingham Tharmarasa;Weihu Zhao;Guopeng Li;Richard Lee;Thiagalingam Kirubarajan;
      Pages: 5768 - 5777
      Abstract: In this article, the problem of tracking vehicles using lidar sensors mounted on an ego-vehicle is addressed. Due to line-of-sight limitations, the back (or front) of a vehicle as seen by the lidar on the ego-vehicle behind (or ahead of) it is often modeled as L-shaped. In this article, an L-shape-based vehicle tracking algorithm with joint kinematic and geometric estimation is presented. By feeding back tracking results to L-shape fitting, an L-shape detection method that is robust to outliers is proposed. In the L-shape tracker currently available in the literature, the kinematic and geometric states of the L-shape model are separately estimated and maintained. However, the kinematic and geometric states are not independent since the orientation of a vehicle influences its velocity. Also, the dependence between the kinematic and the geometric states is caused by anchor-point (the closest point on the vehicle being tracked) switching, which is required during changes in the relative position between vehicles. To address this limitation, the proposed L-shape tracker exploits this dependence and estimates the kinematic and geometric states jointly. The proposed L-shape-model-based tracking algorithm is evaluated and compared with the original algorithm using the real traffic data from the KITTI datasets. The results demonstrate the superiority of the proposed algorithm over the original algorithm in terms of L-shape detection and tracking accuracies.
      PubDate: MON, 24 APR 2023 10:10:32 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Variational Bayesian Inference for DOA Estimation Under Impulsive Noise
           and Nonuniform Noise

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      Authors: Kun Guo;Liang Zhang;Yingsong Li;Tian Zhou;Jingwei Yin;
      Pages: 5778 - 5790
      Abstract: Existing direction-of-arrival (DOA) estimation approaches are often only considering Gaussian noise or impulsive noise, leading to the performance degradation in the scenario that both noises exist simultaneously. Considering that ambient noise of an underwater acoustic array may have different variances due to the large aperture, this article proposes a robust sparse recovery method based on variational Bayesian inference (VBI) that considers the “heavy tailed” characteristics of impulsive noise, and the nonuniformity of ambient noise. Student-t distribution and Bernoulli distribution are modeled as impulsive noise in the measurement, and then, the array observed signal is created as a mixture of desired signal, impulsive noise and nonuniform noise. A VBI scheme is constructed to estimate the desired sparse signal to implement DOAs. Results obtained from the numerical simulation and experimental data processing verify the superior performance of the proposed VBI promoting DOA estimation for dealing with impulsive noise and nonuniform noise.
      PubDate: MON, 10 APR 2023 05:52:49 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Motion Compensation in Six Degrees of Freedom for a MIMO Radar Mounted on
           a Hovering UAV

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      Authors: Philipp Stockel;Patrick Wallrath;Reinhold Herschel;Nils Pohl;
      Pages: 5791 - 5801
      Abstract: This article deals with the motion of a hovering unmanned aerial vehicle (UAV) with six degrees of freedom. The effects of the motion on the measured signal of a multiple-input multiple-output radar mounted underneath the UAV are analyzed. For each degree of freedom, namely the translation in $x$, $y$, and $z$ directions as well as the rotation around $x$, $y$, and $z$ axes, an algorithm is proposed to compensate the considered motion. The effectiveness of the proposed algorithms is demonstrated by measuring validated vital signs independent of the current UAV motion.
      PubDate: WED, 12 APR 2023 10:02:02 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Fault-Tolerant Design of Barrier Coverage for Periodically Repairable
           Wireless Sensor Networks

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      Authors: Alexander Michael Daniel;Ramzi Mirshak;
      Pages: 5802 - 5822
      Abstract: Wireless sensor networks (WSNs) are sometimes deployed in circumstances where failed sensors may not be able to be repaired or replaced immediately after failure, e.g., a remote, harsh environment. By deploying redundant sensors, such networks will be more likely to tolerate individual sensor failures without experiencing an intolerable degradation in performance. This article analyzes the redundancy required in a barrier-covering WSN where repairs to broken sensors can occur at periodic intervals, called “epochs.” Exact, tractable methods are derived for computing the total gap in coverage, the number of gaps in coverage, and the largest gap in coverage in an epoch. Together, the distributions of these metrics allow network designers to select how much redundancy is required to maintain their desired performance with a specified probability. Numerical simulations then demonstrate the accuracy of the methods, and show how network designers can explore the tradeoff between deploying larger numbers of cheap, poor-quality sensors and deploying smaller numbers of expensive, robust sensors.
      PubDate: WED, 12 APR 2023 10:02:02 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Fractionally Delayed Bayesian Approximation Filtering Under Non-Gaussian
           Noisy Environment

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      Authors: Guddu Kumar;Venu Gopal Yamalakonda;R. Swaminathan;Abhinoy Kumar Singh;
      Pages: 5823 - 5834
      Abstract: Gaussian filtering traditionally suffers from two major drawbacks: 1) Gaussian approximation of the intrinsic non-Gaussian measurement noises and 2) ignoring delay in measurements. This article designs an advanced Gaussian filtering algorithm for addressing the two drawbacks and improving the accuracy. The proposed method is abbreviated as GFMCFD, indicating Gaussian filtering under the maximum correntropy (MC) criterion for fractionally delayed measurements. The MC-criterion-based design enables the proposed GFMCFD to handle the non-Gaussian noises. Moreover, to deal with the delay, the proposed GFMCFD stochastically identifies the delay and uses the current measurement to estimate the desired state at a past instant, depending on the preidentified delay. Thereafter, it later updates the estimated state till the current time instant using state dynamics to perform real-time estimation. Interestingly, the proposed GFMCFD considers the delay as a fractional multiple of the sampling interval. The improved accuracy of the proposed GFMCFD is validated for two nonlinear filtering problems.
      PubDate: TUE, 11 APR 2023 10:02:07 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Integration of a Real-Time CCSDS 410.0-B-32 Error-Correction Decoder on
           FPGA-Based RISC-V SoCs Using RISC-V Vector Extension

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      Authors: Yao-Ming Kuo;Mark F. Flanagan;Francisco Garcia-Herrero;Óscar Ruano;Juan Antonio Maestro;
      Pages: 5835 - 5846
      Abstract: The Consultative Committee for Space Data Systems (CCSDS) recommends the use of short-block length Bose–Chaudhuri–Hocquenghem and binary low-density parity-check codes. Despite the high error-correction capacity of nonbinary low-density parity-check (NB-LDPC) codes, they have not yet been considered due to their high decoding complexity. In this article, the feasibility of NB-LDPC coding for space telecommand link applications using an RISC-V soft-core processor plus a vector coprocessor is demonstrated. The purpose of this article is to avoid the need for a dedicated decoder hardware, and thus, the customized general-purpose processor that performs decoding can be reconfigured to perform other important onboard tasks. In this way, the logic utilization and power consumption can be reduced since more functionalities can be assumed by the onboard processor. The method of acceleration of an NB-LDPC decoder over GF(16) using the RISC-V vector extension is demonstrated, and a throughput of 8.48 kb/s is achieved for the forward–backward implementation of the min–max decoding algorithm, which is compatible with the low-rate and mid-rate telecommand systems recommended by the CCSDS.
      PubDate: TUE, 11 APR 2023 10:02:07 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Progressive Keypoint Detection With Dense Siamese Network for SAR Image
           Registration

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      Authors: Deliang Xiang;Yihao Xu;Jianda Cheng;Yuzhen Xie;Dongdong Guan;
      Pages: 5847 - 5858
      Abstract: Synthetic aperture radar (SAR) image registration plays a key role in the applications such as change detection and image mosaic, which is one of the most challenging tasks in recent years due to speckle noise, geometric distortion, and nonlinear radiation differences between SAR images. In the feature-based SAR image registration methods, the repeatability of keypoints and the effectiveness of feature descriptors could directly affect the registration accuracy. In this article, we propose a stable feature intersection-based (FI) keypoint detector, which contains three progressive detectors, i.e., phase coherency detector, horizontal/vertical, major/minor-diagonal-oriented gradient detectors, and local coefficient of variation detector. Our proposed keypoint detector can not only effectively extract keypoints with high repeatability but also greatly reduce the number of false keypoints, thus reducing the computational cost of feature description and matching. Then, we propose the cross-stage partial Siamese network to rapidly extract feature descriptors containing both deep and shallow features, which can be used to obtain more correct matching point pairs than traditional man-made shallow descriptors. In addition, we design a hard example mining loss to minimize the matching distance between the matching descriptor and adaptive selected nonmatching descriptors. Experimental results on different pairs of SAR images demonstrate that our proposed method achieves better performance than other state-of-the-art methods.
      PubDate: TUE, 11 APR 2023 10:02:07 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Multimodality-Aided Multicarrier Waveform Recognition in Low SNR Regimes
           Based on Denoised Cyclic Autocorrelation Transformation

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      Authors: Zeliang An;Tianqi Zhang;Yuqing Xu;Gert Frølund Pedersen;Ming Shen;
      Pages: 5859 - 5875
      Abstract: Wireless signal recognition driven by artificial intelligence (AI) plays a pivotal role in 6G ultrareliable wireless communications, facilitating spectrum surveillance to impede illegal radio interference. As a promising wireless technique, multicarrier waveform recognition (MWR) has been explored to enhance the reliability of wireless data transmission. However, existing works fail to achieve reliable recognition accuracy under strong noise. It remains a daunting task for MWR in signal-to-noise ratio (SNR) regimes. To deal with this issue, we propose a denoised cyclic autocorrelation-based multimodality fusion network (DCA-MFNet). Specifically, we first leverage the cyclic autocorrelation (CA) transformation to convert intercepted signals into CA features in cyclic frequency domains, which have the robust property of being insensitive to low SNR. Next, the singular value decomposition method is employed to weaken the strong noise effects on useful CA peak values. Based on the denoised CA matrix (DCAM), the projection accumulation strategy is proposed to generate the time delay accumulation vector (TDV) and cyclic frequency accumulation vector (CFV), which can enlarge the discrimination among multicarrier signals. Finally, we fed the multimodality features of DCAM, TDV, and CFV into the developed DCA-MFNet to perform hierarchical learning, feature aggregation training, and multicarrier type prediction. Experimental results demonstrate that the proposed DCA-MFNet obtains better recognition performance than existing algorithms. Moreover, DCA-MFNet can effectively identify six multicarrier signals with a recognition accuracy of 100% at a low SNR of even −2 dB.
      PubDate: TUE, 11 APR 2023 10:02:07 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Characterization and Detection of RF Electronics Using Localized Conjugate
           Component

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      Authors: Yuanzhe Li;Weidong Hu;Xiaoyong Du;Hongqi Fan;Beichen Fan;
      Pages: 5876 - 5887
      Abstract: Nonlinear radar has a unique advantage in finding nonlinear targets, like radio frequency (RF) electronics and harmonic tags, under clutter circumstances, among which intermodulation-based schemes are usually favored for their resistance to self-harmonics interference. Currently, intermodulations produced by multiple large signals are dominantly used for nonlinear target characterization and detection. However, this article proposes to exploit the intermodulation generated by the large and small signals, referred to as localized conjugate component (LCC). We observe that the LCC describes the localized nonlinear structure, which contains discriminative representation exclusive to nonlinear targets. Compared with traditional intermodulation detection, LCC is independent of the small signal's waveform and dependent on the power-dependent scattering parameter, which makes it flexible for waveform design and cost effective for performance estimation. Benchtop measurement results validate the aforementioned properties. Furthermore, an over-the-air experiment confirms the feasibility of nonlinear target detection at a stand-off distance using LCC. Its application prospects in target identification are also presented.
      PubDate: FRI, 02 JUN 2023 10:02:45 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Parameter Identification of a PN-Guided Incoming Missile Using an Improved
           Multiple-Model Mechanism

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      Authors: Yinhan Wang;Jiang Wang;Shipeng Fan;
      Pages: 5888 - 5899
      Abstract: This article aims to accurately estimate the state of the incoming missile and effectively implement an evasion maneuver, the parameters of the incoming missile, including a guidance constant and a first-order lateral time constant, should be identified online. To this end, assuming that a missile with proportional navigation guidance law attempts to attack an aerial target with bang-bang evasion maneuvers, a parameter identification model based on the gated recurrent unit neural network is built in this article. The analytic identification solutions for the guidance law parameter and the first-order lateral time constant are derived and show that the identification of the latter parameter is more difficult. The inputs of the identification model are available kinematic information between the aircraft and the missile, while the outputs contain the regression results of the missile’s parameters. To increase the training speed and identification accuracy of the model, an output processing method called the improved multiple-model mechanism (IMMM) is proposed in this article. The effectiveness of IMMM and the performance of the established model are demonstrated through numerical simulations under various engagement scenarios.
      PubDate: MON, 17 APR 2023 10:03:47 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Joint Resource and Detection Threshold Optimization for Maneuvering
           Targets Tracking in Colocated MIMO Radar Network

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      Authors: Yang Su;Ting Cheng;Zishu He;
      Pages: 5900 - 5914
      Abstract: Compared with monostatic phased array radar, a colocated multiple-input–multiple-output (MIMO) radar network (CMRN) that owns richer controllable resources is able to offer superior tracking performance. In this article, for targets tracking in a cluttered environment with the CMRN, a joint detection threshold optimization and transmit resource allocation (JDTO-TRA) algorithm is proposed to simultaneously maximize the tracking performance and minimize the resource consumption, whose key mechanism is to adaptively control the working parameters at both transmitters and detectors via optimization technique. To be more specific, we first formulate the JDTO-TRA strategy as a biobjective optimization problem, where both the tracking performance and the resource consumption are considered in the objective function. The analytical expression for the posterior Cramér–Rao lower bound incorporated with information reduction factor, which is caused by the measurement origin uncertainty, is given and adopted as the tracking performance metric, and the total transmit energy of the CMRN is employed as the resource consumption metric. Hereafter, to tackle the formulated mixed-integer and nonconvex problem effectively, an iterative and efficient two-step solution technique incorporating the simulated-annealing-based hybrid particle swarm optimization and the cyclic minimization algorithm is proposed, where the radar-target assignment, the subarray number and the transmit energy of each activated colocated MIMO radar, and the false alarm rate of the target can be controlled jointly and adaptively. Numerical simulation results are provided to demonstrate the effectiveness as well as the advantages of the JDTO-TRA algorithm compared with other popular existing algorithms.
      PubDate: MON, 17 APR 2023 10:03:47 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Resolving Doppler Ambiguity for Fast-Moving Targets With FDA-MIMO Radar

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      Authors: Yanxing Wang;Shengqi Zhu;Lan Lan;Jingwei Xu;Ximin Li;
      Pages: 5915 - 5929
      Abstract: Although the problem of range ambiguity is solved with the frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radar by utilizing the degree-of-freedom in the range domain, the phenomenon of Doppler ambiguity occurs for the fast-moving target. In this article, an offset frequency alignment method is proposed to solve the problem of Doppler ambiguity in FDA-MIMO radar. At the design stage, the Doppler frequency offsets, corresponding to different transmit pulses, are aligned by eliminating the residual phases. In this regard, the point targets can be focused in the transmit spatial frequency domain. Furthermore, the Doppler ambiguity index is estimated after principal velocity compensation resorting to a maximum likelihood criterion, where both the cases of known and unknown range ambiguity indexes are investigated, respectively. Moreover, in order to ensure the estimation performance, the frequency offset across the transmit array elements is designed by enlarging the term related to the Doppler frequency offset. At the analysis stage, the performance of Doppler ambiguity index estimation in terms of the root-mean-square error is assessed in comparison with the Cramer–Rao bound. Numerical results are provided to verify the effectiveness of the proposed method to resolve the Doppler ambiguity.
      PubDate: MON, 17 APR 2023 10:03:47 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Trajectory Planning for Complex Shaped Spacecraft Proximity Based on
           Critical Safety Curve and Disturbed Fluid

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      Authors: Jingxian Wang;Rong Chen;Zhijun Chen;Yong Zhao;Yuzhu Bai;Xiaoqian Chen;
      Pages: 5930 - 5942
      Abstract: Recent research on on-orbit service rarely considers the complex shapes of servicing spacecraft (chaser) and the target simultaneously, which could cause collision problems in ultraclose-range proximity. This article presents a novel trajectory planning method via critical safety curve (TPCSC). The core of the framework is the construction of collision avoidance constraints called critical safety curve (CSC) using geometrized mathematical methods. CSC has good mathematical properties and innovatively combines the complex shape and relative attitude constraints of two spacecraft. Subsequently, TPCSC proposes an improved disturbed fluid method based on CSC that can handle non-convex constraints to generate smooth approach trajectories. Finally, the framework can achieve ultraclose-range proximity between spacecraft with complex shapes under complex non-convex constraints. The simulation results indicate that the planning of TPCSC is rapid, and the formed trajectory is safe, smooth, and easy to track.
      PubDate: MON, 17 APR 2023 10:03:47 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Onboard Observation Planning for Sequential Image-Based Planetary Landing
           Navigation in Unknown Environments

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      Authors: Jiaxing Li;Dayi Wang;Maodeng Li;Tianshu Dong;Fangzhou Fu;Chao Xu;
      Pages: 5943 - 5960
      Abstract: Autonomous navigation based on sequential images is widely accepted as an effective method to guarantee pinpoint landing and precise obstacle avoidance in planetary landings. Observing a large number of landmarks and processing a large number of images places a computational burden on landers with limited computational resources, but existing studies lack observation planning methods for landmarks in unknown environments that can be operated autonomously on landers. In this article, we analyze the observability of a vision-aided inertial navigation system and propose an observation planning strategy. The minimum number of landmarks and the minimum number of observations are obtained from the observability analysis, which is the boundary condition for observation planning. The landmark configuration planning in the observation planning strategy optimizes only univariate convex functions to efficiently select a small number of landmarks in unknown environments with high navigation accuracy, and the observation interval planning adaptively reduces the number of observations without significantly affecting the navigation accuracy. Simulation results verify the correctness of the observability analysis results, and it is found that the proposed observation planning strategy outperforms traditional observability degrees in terms of both improved navigation accuracy and computational speed when observing a small number of landmarks and can effectively reduce the number of observations.
      PubDate: MON, 17 APR 2023 10:03:47 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory

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      Authors: Jin Sob Kim;Hyun Joon Park;Wooseok Shin;Dongil Park;Sung Won Han;
      Pages: 5961 - 5977
      Abstract: The automatic identification system (AIS) has recorded near-real-time vessel monitoring data over the years, paving the way for data-driven maritime surveillance methods; concurrently, the data suffer from unrefined, reliability issues, and irregular intervals. In this article, we address the problem of vessel destination estimation by exploiting the global-scope AIS data. We propose a differentiated data-driven approach recasting a long sequence of port-to-port international vessel trajectories as a nested sequence structure. Based on spatial grids, this approach mitigates the spatio-temporal bias of AIS data while preserving the detailed resolution of the original. Further, we propose a novel deep learning architecture (WAY) that is designed to effectively process the reformulated trajectory and perform the long-term estimation of the vessel destination ahead of arrival with a horizon of days to weeks. WAY comprises a trajectory representation layer and channel-aggregative sequential processing (CASP) blocks. The representation layer produces the multichannel vector sequence output based on each kinematic and nonkinematic feature collected from AIS data. Then CASP blocks include multiheaded channel- and self-attention architectures, where each processes aggregation and sequential information delivery, respectively. Then, a task-specialized learning technique, gradient dropout (GD), is also suggested for adopting many-to-many training along the trajectory progression on single labels. The technique prevents a surge of biased feedback by blocking the gradient flow stochastically using the condition depending on the length of training samples. Experimental results on five-year accumulated AIS data demonstrated the superiority of WAY with recasting AIS trajectory compared to conventional spatial grid-based approaches, regardless of the trajectory progression steps. Moreover, the data proved that adopting GD in a spatial grid-based approach leads to the performance gain. In addition, the possibilities of improvement and real-world application with WAY's expandability in multitask learning for the estimation of estimated time of arrival was explored.
      PubDate: TUE, 25 APR 2023 10:02:32 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Direction Finding in Partly Calibrated Arrays Exploiting the Whole Array
           Aperture

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      Authors: Guangbin Zhang;Tianyao Huang;Yimin Liu;Xiqin Wang;Yonina C. Eldar;
      Pages: 5978 - 5992
      Abstract: We consider the problem of direction finding using partly calibrated arrays, a distributed subarray with position errors between subarrays. The key challenge is to enhance angular resolution in the presence of position errors. To achieve this goal, existing algorithms, such as subspace separation and sparse recovery, have to rely on multiple snapshots, which increases the burden of data transmission and the processing delay. Therefore, we aim to enhance angular resolution using only a single snapshot. To this end, we exploit the orthogonality of the signals of partly calibrated arrays. Particularly, we transform the signal model into a multiple-measurement model, and show that there is approximate orthogonality between the source signals in this model. We then use blind source separation to exploit the orthogonality. Simulation and experiment results both verify that our proposed algorithm achieves high angular resolution as distributed arrays without position errors, inversely proportional to the whole array aperture.
      PubDate: TUE, 25 APR 2023 10:02:34 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Multiround Message Scheduling for Fast GNSS Packet Broadcasting

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      Authors: Francesco Ardizzon;Nicola Laurenti;Stefano Tomasin;
      Pages: 5993 - 6004
      Abstract: Global navigation satellite systems (GNSSs) may transmit data to provide additional services. Since the resources reserved for these data are typically fixed and the message rate is low, we propose to split long messages into shorter packets and properly schedule their transmission from the entire constellation of satellites. Considering an efficient data transmission (useful for example for search and rescue messages), we aim at the scheduling of packets on the satellites on multiple rounds with two objectives: 1) the minimization of the maximum latency among all receivers or 2) the maximization of the average received packets per round. We first derive bounds on the performance of any GNSS single or multiround scheduling solution, on which the proposed scheduling solutions are based. Then, we introduce the scheduling problems that turn out to be integer linear programming problems. Lastly, we assess their performance, showing that our solution minimizes the maximum latency, while the scheduling targeting the average latency outperforms existing literature solutions.
      PubDate: MON, 24 APR 2023 10:10:32 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Systematic Method for Constrained Attitude Control Under Input
           Saturation

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      Authors: Chengfei Yue;Tao Huo;Ming Lu;Qiang Shen;Chaoyong Li;Xueqin Chen;Xibin Cao;
      Pages: 6005 - 6015
      Abstract: In this article, the constrained attitude control problem with a prescribed performance requirement is investigated, and a systematic method involving the synthesis of path planning, prescribed performance design, and tracking control with inherent saturation is proposed. The overall work is based on an attitude rotational matrix and its equivalent vector description. In the path planning phase, both the solid constraints, referring to the forbidden region where the sensitive instrument is strictly prohibited, and the soft constraints, referring to a designable margin of safety, are considered. Then, a centric reference trajectory outside the soft constraints is established according to a gradient-based algorithm. To formulate the reference trajectory, a motion planning method is employed to establish the ideal angular velocity constrained by the maximum affordable control torque. In the prescribed performance design phase, the performance boundary tube is generated to avoid any potential invasion caused by the attitude tracking error. Finally, in the controller design phase, inherent input saturation is considered, and a magnitude-constrained controller is proposed. When the proposed controller is exerted on the system, closed-loop system stability is achieved and the spacecraft is always kept outside the solid constraints. The effectiveness of the proposed method is verified through numerical simulations.
      PubDate: THU, 20 APR 2023 10:06:54 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Signal Structure of the Starlink Ku-Band Downlink

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      Authors: Todd E. Humphreys;Peter A. Iannucci;Zacharias M. Komodromos;Andrew M. Graff;
      Pages: 6016 - 6030
      Abstract: In this article, we develop a technique for blind signal identification of the Starlink downlink signal in the 10.7 to 12.7-GHz band and present a detailed picture of the signal's structure. Importantly, the signal characterization offered herein includes the exact values of synchronization sequences embedded in the signal that can be exploited to produce pseudorange measurements. Such an understanding of the signal is essential to emerging efforts that seek to dual-purpose Starlink signals for positioning, navigation, and timing, despite their being designed solely for broadband Internet provision.
      PubDate: MON, 24 APR 2023 10:10:32 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Event-Triggered Sliding Mode Control for Spacecraft Reorientation With
           Multiple Attitude Constraints

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      Authors: Jin Tan;Kai Zhang;Bin Li;Ai-Guo Wu;
      Pages: 6031 - 6043
      Abstract: The article addresses the event-triggered attitude control problem for spacecraft anti-unwinding reorientation with multiple attitude constraints in the presence of external disturbance. Based on a designed novel potential function (PF), a new sliding mode controller is proposed to guarantee the completeness of anti-unwinding reorientation with multiple attitude constraints and external disturbance. Different from the existing continuous attitude reorientation controller, the static and dynamic event-triggered mechanisms, related to the sliding mode variable and disturbance bound, are then constructed. With the designed event-triggered sliding mode control schemes, the attitude system asymptotic stability, the satisfaction of attitude constraints with anti-unwinding action, and free of Zeno phenomenon despite external disturbance are proved theoretically. Numerical simulation results confirm that the proposed methods can significantly reduce resource usage while guaranteeing the desired performance.
      PubDate: WED, 26 APR 2023 10:06:27 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Beam Scheduling for Early Warning With Distributed MIMO Radars

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      Authors: Dengsanlang Luo;Gongjian Wen;Yuanyuan Liang;Lingxiao Zhu;Haibo Song;
      Pages: 6044 - 6058
      Abstract: Distributed multiple-input multiple-output (MIMO) radars achieve long-range detection by steering and focusing the narrow beams of all transmit and receive antennas on a single point. This article proposes a beam scheduling scheme based on the joint optimization of three components: arrangement of focal points, beam dwell time allocation of each focal point, and scanning order of focal points. The purpose is to enable distributed MIMO radars to provide early warning of incursions entering a broad surveillance region. To this end, a mathematical model is first developed to evaluate radar detection capability in a cell under test (CUT) over a scheduling period. By integrating all the CUTs within the region, the detection time required to reach a specified performance in terms of cumulative detection probability is then derived. On this basis, a beam scheduling strategy is devised, considering the maximum speed and minimum average radar cross-section constraints of targets to be detected. The main mechanism of the strategy is to minimize the detection time without sacrificing performance. To address the optimization problem of beam scheduling, we propose a two-stage method using a detection capability threshold as an extra optimization variable. In our method, the first stage is used to obtain optimal schemes for different given thresholds, and in the next stage, the optimal threshold is determined from these schemes. The simulation results show that the proposed algorithm is effective.
      PubDate: TUE, 02 MAY 2023 10:14:46 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Dynamic Game Strategy for Radar Screening Pulsewidth Allocation Against
           Jamming Using Reinforcement Learning

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      Authors: Pengfei Liu;Lei Wang;Zhao Shan;Yimin Liu;
      Pages: 6059 - 6072
      Abstract: Radio frequency (RF) screening is a practical antijamming technique. Determining screening pulse width is key to the performance of RF screening. In this article, we investigate the problem of radar screening pulsewidth allocation against a cognitive jammer, which also seeks a strategy to best jam the radar. As both the radar and the jammer are taken as intelligent agents learning to optimize their respective performance, dynamic game theory is employed and the problem is constructed as an extensive-form game. A strategy learning approach based on reinforcement learning is proposed to approximate the Nash equilibrium (NE). Simulation results verify that the proposed learning approach outperforms existing ones and that the learned strategies approximate the NE.
      PubDate: THU, 27 APR 2023 10:10:33 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Covariance Analysis of the Optimal Orbital Interception With Navigation
           Errors

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      Authors: Gang Zhang;Huidong Ma;
      Pages: 6073 - 6086
      Abstract: This article derives the analytical linear covariances for the optimal single-impulse orbital interception problems with navigation errors. The initial position and velocity errors are considered for both the interceptor and the nonmaneuvering target. Firstly, for free impulse time and free interception time, the necessary conditions for the minimum-fuel and the minimum-time interceptions are derived, and the optimal solutions are obtained by 2-D numerical search methods. Then, the linear covariance method is used to derive the error covariances for the minimum-fuel solution and the minimum-time solution. Finally, based on the first-order and second-order partial derivatives, the error covariances of the optimal impulse time, the optimal interception time, the required impulse vector, and the interception position vector are analytically obtained. Numerical results indicate that the proposed analytical linear covariance method is accurate for both the minimum-fuel and the minimum-time interception problems.
      PubDate: TUE, 25 APR 2023 10:02:34 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Data-Driven Optimal Formation-Containment Control for a Group of
           Spacecrafts Subject to Switching Topologies

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      Authors: Ming Cheng;Hao Liu;Yan Wan;Kimon P. Valavanis;Frank L. Lewis;
      Pages: 6087 - 6097
      Abstract: The research focuses on the data-driven optimal time-varying formation-containment control problem for a group of heterogeneous spacecrafts that are simultaneously subjected to switching topologies, nonlinear relative motion dynamics, and parameter variations. A distributed observer is developed for each spacecraft to generate the position references and to decrease the influences of switching topologies. The optimal formation-containment control protocol is learned using relative motion data and system inputs, based on the reinforcement learning theory. Simulations of a spacecraft group are given to show the performance of the proposed formation-containment control protocol.
      PubDate: TUE, 25 APR 2023 10:02:32 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Recognition and Estimation for Frequency-Modulated Continuous-Wave Radars
           in Unknown and Complex Spectrum Environments

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      Authors: Kuiyu Chen;Jingyi Zhang;Si Chen;Shuning Zhang;Huichang Zhao;
      Pages: 6098 - 6111
      Abstract: The recognition and estimation of frequency-modulated continuous-wave (FMCW) radar signals are critical for both military electronic countermeasures and civilian autonomous driving. However, the increasingly complex radio environment poses two new challenges for detection equipment. First, multiple FMCW radars can share the same frequency band, resulting in an overlap of received FMCW signals in the time–frequency domain. Second, unexpected signals in unknown spectrum environments can affect the cognitive performance of FMCW signals. This article proposes a semantic-based learning network (SLN) that simultaneously learns modulation classification and parameter regression of FMCW signals. By integrating recognition and estimation into a single network, the system can be optimized end-to-end as a whole. Additionally, instance-level semantic learning facilitates the parallel analysis of multiple components in overlapping signals. Finally, contrastive clustering in SLN achieves suppression of unexpected signals. Numerous comparative experimental results demonstrate that SLN has the desirable ability to simultaneously recognize and estimate FMCW signals in real-time, even in unknown spectrum environments.
      PubDate: FRI, 05 MAY 2023 10:28:11 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Fast Algorithm for Onboard Atmospheric Powered Descent Guidance

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      Authors: Yushu Chen;Guangwen Yang;Lu Wang;Haipeng Chen;Qingzhong Gan;Quanyong Xu;
      Pages: 6112 - 6123
      Abstract: Atmospheric powered descent guidance (APDG) can be solved by successive convexification; however, its onboard application is impeded by high computational cost. When aerodynamic forces are ignored, powered descent guidance (PDG) can be converted to a single convex problem. In contrast, APDG has to be converted into a sequence of convex subproblems, each of which is significantly more complicated. Consequently, the computation increases sharply. A fast real-time interior point method was presented to solve the correlated convex subproblems efficiently onboard in the work. The main contributions are as follows: First, an algorithm was proposed to accelerate the solution of linear systems that cost most of the computation in each iterative step by exploiting the specific problem structure. Second, a warm-starting scheme was introduced to refine the initial value of a subproblem with a rough approximate solution of the former subproblem, which lessened the iterative steps required for each subproblem. The method proposed reduced the run time by a factor of 9 compared with the fastest publicly available solver tested in Monte Carlo simulations to evaluate the efficiency of solvers. Runtimes on the order of 0.6 s are achieved on a radiation-hardened flight processor, which demonstrated the potential of the real-time onboard application.
      PubDate: WED, 10 MAY 2023 10:02:03 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Long-Term Evolution of the Space Environment Considering Constellation
           Launches and Debris Disposal

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      Authors: Jingrui Zhang;Yurun Yuan;Keying Yang;Lincheng Li;
      Pages: 6124 - 6137
      Abstract: Increasingly frequent launch activities, as well as the development of mega constellations, would cause a drastic increase in the number of space objects, which will then alter the evolution of outer space. To reveal this long-term change, an accurate space-environment model is required. There are two main approaches to building this model, one of which is to track the state of space objects individually, which will use significant computing resources; and the other is to take macroscopic variables, such as spatial density, as the state variable to depict a group of space debris, which requires less computational effort. In this article, a space debris environment evolution model with spatial density as the state variable is established, which considers the nonzero eccentricity of the debris orbit and utilizes the NASA breakup model to ensure accuracy. In addition, the Gaussian mixture model is applied to take the uncertainty of launch activities into account. The long-term impacts of mega constellations and their postmission disposal (PMD) on the debris environment are discussed based on the evolution model. It was found that constellations with high orbit altitude, such as OneWeb, will lead to an exponential increase in space objects in low Earth orbit. In addition, deorbit time is the main factor affecting the PMD efficiency, followed by deorbit strategies.
      PubDate: MON, 08 MAY 2023 10:14:03 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Deployment for NOMA-UAV Base Stations Based on Hybrid Sparrow Search
           Algorithm

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      Authors: Jiamei Chen;Xinyue Li;Jing Xu;Yao Wang;
      Pages: 6138 - 6149
      Abstract: Auxiliary UAV base stations are expected to be the most promising schemes to fill the resource shortage and the blind spot of coverage of the ground base stations in the future. This article proposes a hybrid sparrow search algorithm (SSA) to optimize the three-dimensional (3D) deployment of multi-UAV base stations with the objective of maximizing the sum log-rate utility. First, the air-to-ground channel model is established using the nonorthogonal multiple access technology to improve the spectrum utilization by considering the complex interference forms in multi-UAV scenarios, which is more compatible with the actual applications. Then, the multi-UAV deployment problem is described as an optimization problem to maximize the sum log-rate utility of all users. Finally, a hybrid SSA is proposed to implement the optimal 3D deployment of multi-UAV base stations. Specifically, in order to overcome the local optimization defects of the traditional SSA, the opposition-based learning strategy is utilized to modify the initial position of the sparrows to promote particle diversity, and the sine-cosine algorithm is developed to optimize the update formula of the beggar sparrows by integrating the disturbance into the update process. Consequently, a hybrid SSA with higher search accuracy, stability, and robustness is formed. The simulation results demonstrate that the proposed method outperforms the traditional methods in terms of the sum log-rate utility and throughput.
      PubDate: TUE, 02 MAY 2023 10:14:46 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Digital Prototype Filter Alternatives for Processing Frequency-Stacked
           Mobile Subbands Deploying a Single ADC for Beamforming Satellites

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      Authors: Adem Coskun;Sevket Cetinsel;Izzet Kale;Robert Hughes;Piero Angeletti;Christoph Ernst;
      Pages: 6150 - 6163
      Abstract: This article presents a two-stage approach for the processing of frequency-stacked mobile subbands. The frequency stacking is performed in the analog domain to enable the use of a wideband analog-to-digital converter (ADC), instead of employing multiple narrowband ADCs, to support multiple antenna elements for digital satellite beamforming. This analog front end provides a common broadband digital interface to the on-board processor and can be configured to support multiple satellite missions, reducing the cost of commissioning a digital processor for individual satellite missions. This article proposes a framework on the specification of digital prototype filter for the analysis of frequency-stacked mobile subbands. The computational complexity of the analysis operation, with two digital filter alternatives, are evaluated. A series of results, taken from our European Space Agency sponsored project, are presented here to demonstrate the applicability of the proposed two stage approach, reporting on the savings in power consumption when an Nth-band all-pass-based recursive filter having an infinite impulse response is used as the digital prototype filter.
      PubDate: FRI, 12 MAY 2023 10:01:23 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Deeply Coupled GNSS Dual-Band Collaborative Tracking Using the Extended
           Strobe Correlator

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      Authors: Jiawei Xu;Rong Yang;Zuping Tang;Xingqun Zhan;Y. Jade Morton;
      Pages: 6164 - 6178
      Abstract: Continuous and precise code delay tracking is essential in challenging environments such as urban canyons where multipath and frequent signal blockage may lead to performance degradation and loss of lock of signals. The strobe correlator (SC) is an effective technique for baseband multipath mitigation and is commonly used for GNSS signal tracking enhancement. An SC has two pairs of early and late correlators, one with a wide and another with a narrow correlator spacing. This article extends the conventional SC design by applying its correlator pairs on dual-band GNSS signals to realize an extended strobe correlator (ESC). An ESC takes advantage of the natural distinct bandwidth of narrow-band and wide-band signals, e.g., L1 C/A and L5Q signals, to form a dual-band combination at the correlator level in a deeply coupled manner. Narrow-band L1 signal tracking ensures robustness, while the wide-band L5 signal improves the tracking precision. A moving average window filters the L1 measurement to prevent the noise impact on L5 tracking, providing necessary interband assistance to resolve challenges such as code delay misalignment and precision mismatch. Through both Monte Carlo simulation and real data collected in urban environments, this work demonstrates that an ESC can achieve improved overall robustness against loss-of-lock and multipath mitigation performance over standalone tracking. Compared with KF-based combined (KFC-based) tracking, ESC has higher tracking precision under weak signal environment and robustness during signal blockage, but larger residual multipath error.
      PubDate: THU, 11 MAY 2023 10:01:25 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Study of Licklider Transmission Protocol in Deep-Space Communications in
           Presence of Link Disruptions

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      Authors: Lei Yang;Jie Liang;Ruhai Wang;Xingya Liu;Mauro De Sanctis;Scott C. Burleigh;Kanglian Zhao;
      Pages: 6179 - 6191
      Abstract: Delay/disruption-tolerant networking (DTN) is widely recognized as a key internetworking technology for implementing a space system of systems (SoS). Aimed at being the main data transport protocol of DTN, Licklider transmission protocol (LTP) is expected to provide reliable data transmission service in a challenging space SoS networking environment regardless of random link disruptions. In this article, a study of LTP in deep-space communication networks in presence of link disruptions is presented. An analytical framework is developed for the effect of multiple random link disruptions on LTP's reliable data delivery in deep-space networks. The study indicates that the models accurately predict the transmission performance of LTP over a deep-space communication channel in presence of multiple link disruptions. A threshold interval value, obtained from the analytical modeling results, is proposed as a means of determining the reducibility of two successive link disruptions to a single disruption; that threshold value is verified by the experiments.
      PubDate: MON, 08 MAY 2023 10:13:49 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Distributed Clustering for Secure Multitask Estimation Based on Adaptive
           Task Switching

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      Authors: Feng Chen;Jiapeng Li;Yuanyuan Zhang;Xiaodan Shao;Shukai Duan;
      Pages: 6192 - 6204
      Abstract: Consider adversarial multitask networks with a high cost of sharing genuine information, where agents feed others unreliable information according to a time-varying probability caused by a high privacy cost. This makes agents in the network tend to be selfish, and it greatly increases the difficulty of effective information sharing and distributed multitask estimation without the prior cluster information. To address these problems, we propose an adaptive task-switching strategy that is governed by our formulated information sharing model, which cooperates with the proposed distributed adaptive task-switching learning and clustering for secure estimation over adversarial multi-task network (DATSLCS-AM) algorithm. This distributed algorithm guides agents to cooperate reasonably following reputation updating and pseudoclustering, which can improve the effectiveness of information sharing and also increase the security of estimation over adversarial multitask networks. The theoretical analysis of the adaptive pseudoclustering threshold is provided. Finally, extensive simulations validate the superior performance of our distributed algorithm.
      PubDate: WED, 03 MAY 2023 10:06:46 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Challenges in Automotive MIMO Radar Calibration in Anechoic Chamber

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      Authors: Dani Raphaeli;Igal Bilik;
      Pages: 6205 - 6214
      Abstract: Radars are a major sensor for autonomous driving and active safety. High-angular resolution is the main challenge of the state-of-the-art automotive multiple input multiple output (MIMO) radars. Therefore, MIMO radar sensor array calibration is critical for automotive radar efficiency. Anechoic chamber-based methods for automotive radar testing and calibration are widely used for automotive applications. These methods typically involve a radar target simulator with separate transmit and receive antennas. Frequently, a radar target simulator is located within a relatively small anechoic chamber, compromising the automotive radar's far-field operation conditions. Furthermore, displaced transmit and receive antennas of the radar target simulator induce differences in the steering direction from the radar toward them. This work investigates the effects of these phenomena on automotive radar performance, proposes a method to model them, and introduces the approach to mitigate them.
      PubDate: FRI, 05 MAY 2023 10:28:11 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Robust Multiplexed Piecewise Affine MPC-Based Decentralized Control of
           Multisatellite Formations Using Aerodynamic Forces

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      Authors: Yuandong Hu;Zhengliang Lu;Keck Voon Ling;Wenhe Liao;Xiang Zhang;
      Pages: 6215 - 6232
      Abstract: Aerodynamic forces are well suited to be exploited for low Earth orbit satellite formation control. However, some critical gaps still exist in control schemes suitable for multisatellite formation applications, especially when facing atmospheric environmental uncertainties. This article proposes a scalable and flexible decentralized control scheme for the multisatellite formation using only aerodynamic forces. This scheme adopts a strategy of updating the control inputs alternatively and is applicable to various formation configurations with different architectures without increasing the computational complexity and communication pressure of each satellite. Aerodynamic forces are changed by reorienting the satellite, and the linear formation control model with pointing angles as inputs is obtained by the piecewise affine (PWA) approximation method. A decentralized control scheme is proposed in which each satellite only has to communicate with its neighboring satellites and update its own inputs. This control scheme allows various generalizations of formation architectures, such as one that includes local chief satellites. To deal with model uncertainties, an improved model predictive control (MPC) algorithm named robust PWA-multiplexed MPC is developed using the constraint tightening approach, and then its feasibility and stability issues are analyzed. Hard-in-the-loop simulations are carried out for formation maintenance and reconfiguration, in which aerodynamic force uncertainties are considered.
      PubDate: TUE, 16 MAY 2023 10:01:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • ISAR Imaging of Targets Exhibiting Micromotion Under the Joint Constraints
           of Low SNR and Sparse Rate

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      Authors: Yanbo Mai;Shuanghui Zhang;Weidong Jiang;Chi Zhang;Kai Huo;Yongxiang Liu;
      Pages: 6233 - 6249
      Abstract: To solve the problem of the defocusing of inverse synthetic aperture radar (ISAR) image of targets exhibiting micromotion under the joint constraints of low signal-to-noise ratio (SNR) and sparse rate, this article proposes a method based on the joint constraints of noise and prior information of target. We use the l2 norm to eliminate the noise, and constrain prior information of target (low rank and sparsity) by using nuclear norm and l1 norm. On this basis, we deduce the solution of this problem according to the linear alternating direction method of multipliers. Finally, the proposed method is verified by simulated and measured data. The results show that the proposed method can achieve good focus on the ISAR image of targets exhibiting micromotion under the joint constraints of low SNR and sparse rate, and is much more robust than the traditional methods.
      PubDate: FRI, 05 MAY 2023 10:28:25 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Interference Suppression Method With MR-FDA-MIMO Radar

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      Authors: Zhixia Wu;Shengqi Zhu;Jingwei Xu;Lan Lan;Mengdi Zhang;
      Pages: 6250 - 6264
      Abstract: In the conventional array radar, the suppressible interference is generally limited by the array element number, referred to as the degree of freedom (DOF) of the array. Similarly, the suppressible number of deceptive interference is limited by the transmit element number of frequency diverse array (FDA) multiple-input multiple-output (MIMO) radar. In this article, an interference suppression method based on data-independent beamforming is proposed with FDA-MIMO radar by using a minimum redundancy array (MRA) design in its transmit port, referred to as MR-FDA-MIMO radar. This break through the limitation on the number of suppressible deceptive interferences. The MRA is employed to obtain extended DOF by using difference coarray, resulting in the suppressible number of order O(2M) with M transmit element number. The beamforming method consists of two manifolds: 1) in the receive beamforming stage, the possible target is preserved by using orthogonal subspace projection and the sidelobe barrage interference is suppressed; and 2) in the equivalent transmit beamforming stage, possible nonideal factors are taken into consideration for constructing the covariance matrix to widen the notches corresponding to the predicted deceptive interference. The proposed method outperforms in terms of number of the suppressible deceptive interferences and the signal-to-interference-plus-noise ratio, which is demonstrated with simulation examples.
      PubDate: THU, 18 MAY 2023 10:00:54 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Enhanced BNC Approach for Noncircular Signals Direction Finding With
           Sparse Array in the Scenario of Impulsive Noise

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      Authors: Xudong Dong;Meng Sun;Jun Zhao;Xiaofei Zhang;Yide Wang;
      Pages: 6265 - 6277
      Abstract: Sparse array is popular in the field of array signal processing. However, in the direction of arrival (DOA) estimation, most research on sparse array assumes Gaussian noise, resulting in a model mismatch in the practical scenarios of impulsive noise. We investigate the estimation performance of the bounded nonlinear covariance (BNC) matrix-based methods, which can be used to replace the original data covariance matrix of the received signal and achieve good robustness to the impulsive interference. We propose an enhanced BNC (EBNC) matrix with a noncircular signal for a nested array in the scenario of impulsive noise. The proposed EBNC matrix can fight against the impulsive noise outliers and its boundedness and convergence are shown. The proposed method's performance is assessed with simulations. Simulation results indicate that the proposed method provides better performance in DOA estimation than the classical BNC, correntropy-based covariance matrix, and Gaussian covariance-based methods, especially in highly impulsive noise scenario.
      PubDate: MON, 15 MAY 2023 10:02:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • High-Resolution Imaging and Micromotion Feature Extraction of Space
           Multiple Targets

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      Authors: Lixun Han;Cunqian Feng;
      Pages: 6278 - 6291
      Abstract: In radar applications, different micromotion forms can be used as the basis of target recognition. However, the radar echo signal of multiple spatial targets is overlapping in the time-frequency and time-range domains, which increases the difficulty of micromotion feature extraction. In this article, a high-resolution imaging and micromotion feature extraction framework based on a multiple joint-domain radar tool is proposed to address this mixed signal. First, an accurate and suitable micromotion model of cone-shaped space multiple targets is built. Then, the 2-D adaptive regularized smoothed L0 norm algorithm based on sparse reconstruction is formulated to directly reconstruct the inverse synthetic aperture radar (ISAR) image. In addition, the range-frequency-time radar data cube can be extracted from the ISAR movie by the CLEAN algorithm. To solve the scattering point association problem in the radar data cube, a 3-D segmentation Viterbi algorithm is designed to extract the micromotion features. Finally, simulation and experiment results demonstrate the effectiveness of the proposed framework.
      PubDate: MON, 08 MAY 2023 10:14:03 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Reduced-Complexity Subarray-Level Sparse Recovery STAP for Multichannel
           Airborne Radar WGMTI Application

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      Authors: Ning Cui;Kun Xing;Zhongjun Yu;Keqing Duan;
      Pages: 6292 - 6313
      Abstract: For multichannel airborne radars, wide-area ground-moving target indication (WGMTI) processing can quickly obtain the dynamic distribution of moving targets in a wide area, which holds considerable significance in many fields. Nevertheless, the WGMTI mode suffers from the interference of powerful ground clutter, which frequently submerges slow-moving targets and causes many false alarms in subsequent moving target detection. Space–time adaptive processing (STAP) can successfully suppress clutter, but its performance depends critically on the available training samples. Consequently, an effective STAP method characterized by fast processing and a small sample size for WGMTI applications in multichannel airborne radars must be developed. In this article, a subarray-level sparse recovery STAP processing framework is proposed for multichannel airborne radars. First, the characteristics of the subarray-level received clutter are discussed in detail. Second, on the basis of this analysis, we further designed a joint space–time dictionary and developed a separable tensor-based sparse Bayesian learning (STSBL) method. In this method, two-stage decomposition is proposed to ensure that large-scale data can be degraded into small-scale data in processing, which significantly improves computation efficiency. Finally, the effectiveness of the proposed STSBL-STAP method in WGMTI processing was verified using real measurement data obtained from a developed dual-channel Ku-band airborne radar.
      PubDate: MON, 08 MAY 2023 10:13:49 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Variable False Alarm Rate Detection Framework for Phased Array Radar

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      Authors: Maosen Liao;Guolong Cui;Kui Xiong;Tao Fan;Xianxiang Yu;
      Pages: 6314 - 6326
      Abstract: This article considers a low signal-to-noise ratio (SNR) target detection problem for phased array radar, where classical constant false alarm rate detection framework would cause missed detection. To overcome this problem, we propose a variable false alarm rate detection framework for the phased array radar to ensure the detection performance for low-SNR targets in multistage target detection. First, exploiting the previous detected results, the searching area of interest is divided into multiple subareas, while automatically reducing the false alarm probability of each range section of each frame. Then, a joint allocation strategy of beam and time resources accounting for maximizing the weighted sum of SNRs of spatial sectors in the whole surveillance area of the phased array radar along with some practical coupled constraints, is proposed. Next, the fuzzy analytic hierarchy process and convex optimization methods are, respectively, developed to obtain beam and time resource allocation scheme. Finally, the effectiveness and superiority of the proposed framework are highlighted and evaluated by numerical simulations.
      PubDate: WED, 10 MAY 2023 10:02:03 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Appointed-Time Control for Flexible Hypersonic Vehicles with Conditional
           Disturbance Negation

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      Authors: Jinlin Sun;Zhiqiang Pu;Yafei Chang;Shihong Ding;Jianqiang Yi;
      Pages: 6327 - 6345
      Abstract: This article investigates the antidisturbance control design for flexible air-breathing hypersonic vehicles (FAHVs) with appointed-time prescribed tracking performances. The challenging issues include multisource disturbances, cross-coupling effects of the vehicle dynamics, and asymmetric amplitude and rate saturations. To address these issues, we propose an appointed-time prescribed performance control scheme for FAHVs via conditional disturbance negation technique. In contrast to existing disturbance rejection approaches, the proposed control scheme not only estimates the disturbances first in a fixed time but also evaluates the estimated disturbances and selectively conducts compensation actions according to the insight of the FAHV dynamic characteristics. To further enhance convergence rate, the composite appointed-time prescribed performance controllers are designed for FAHVs via time-varying barrier Lyapunov function and nonsmooth backstepping technique, which ensure satisfactory transient response and steady-state performances. In addition, the asymmetric amplitude and rate saturation problem of actuators are dealt with by introducing unified approximation dynamics. It is rigorously proved that the practical appointed-time convergence of the tracking errors and the fixed-time convergence of all signals in the resultant FAHV closed-loop system can be achieved under the proposed control scheme. Finally, extensive comparative simulations are provided to demonstrate the feasibility and superiority of the proposed approach.
      PubDate: MON, 08 MAY 2023 10:13:49 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • MF-GCN: Motion Flow-Based Graph Network Learning Dynamics for Aerial IR
           Target Recognition

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      Authors: Xi Yang;Shaoyi Li;Bin Cai;Zhongjie Meng;Jie Yan;
      Pages: 6346 - 6359
      Abstract: The primary technology of the IR imaging seeker optoelectronic countermeasure system includes the automatic recognition and tracking of air targets. New complex IR interference factors pose severe challenges to the ability of IR thermal imaging seekers to identify and lock targets accurately. Despite significant recent advances, machine vision systems still lag substantially behind biological vision systems in terms of performance and robustness. The distinguishing feature of the brain is its ability to detect learned objects under various nonideal situations. Inspired by the sensitivity of the dorsal visual pathway to object motion information, a graph convolutional network learning model is developed for aerial target recognition, called the adaptive graph reasoning method driven by a motion flow model, which employs the predicted object optical flow information to promote the keypoint estimation performance. First, an adaptive graph fusion module is designed to establish an adaptation mechanism to generalize keypoint features from the regular network to the target skeleton graph model. Then, the optical flow estimation features are utilized to understand the graph reasoning of the target keypoint motion. Finally, we propose an aerial target pose estimation framework which effectively integrates the temporal cues across frames by combining the motion features of optical flow estimation, and effectively promotes the estimation accuracy of keypoints. Finally, our extensive experiments in the established aerial target flight IR dataset show the efficiency of the presented model.
      PubDate: MON, 08 MAY 2023 10:13:49 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Adaptive Neural Network Extended State Observer-Based Finite-Time
           Convergent Sliding Mode Control for a Quad Tiltrotor UAV

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      Authors: Suiyuan Shen;Jinfa Xu;Pei Chen;Qingyuan Xia;
      Pages: 6360 - 6373
      Abstract: The internal and external disturbance of the quad tiltrotor unmanned aerial vehicle (UAV) stem from its rotor tilting motion and wind disturbance, which affect the control stability of the quad tiltrotor UAV in full flight. This article proposes a control law design method based on the adaptive neural network extended state observer-based finite-time convergent sliding mode control (ANNESO-FTCSMC). The adaptive neural network obtains the unknown total disturbance term that needs to be compensated in the finite-time convergent sliding mode control. An extended state observer is used to estimate the state variables of the controlled plant. The stability of the ANNESO-FTCSMC controller is proved with the Lyapunov stability theory. The unknown total disturbance as the expanded state variable of the state observer has the advantage of high adaptability, and the ANNESO-FTCSMC has a strong ability to compensate for the internal and external disturbances of the system. Compared with traditional sliding mode control, ANNESO-FTCSMC does not contain switching terms, so ANNESO-FTCSMC is chattering-free and converges faster. Based on this method, the flight control system of the quad tiltrotor UAV is designed and verified by hardware-in-loop simulation. The hardware-in-loop simulation results of attitude control in different flight modes and trajectory tracking control in full flight show that the ANNESO-FTCSMC controller is suitable for the flight control of quad tiltrotor UAV. Compared with the PID controller and ADRC controller, ANNESO-FTCSMC controller has more stable flight state of quad tiltrotor UAV under different flight modes and the same total disturbance. The transition process of ANNESO-FTCSMC controller from helicopter mode to airplane mode is also smoother and will not have large fluctuations. Therefore, ANNESO-FTCSMC controller has strong antidisturbance and robustness.
      PubDate: WED, 10 MAY 2023 10:02:03 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • CPAD: Component Pixel-Aware Multispectral Image Destriping

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      Authors: Guoxia Xu;Lizhen Deng;Hao Wang;Hu Zhu;
      Pages: 6374 - 6386
      Abstract: Stripe noise removal of multispectral images (MSIs) is a challenging topic and has attracted substantial research attention in remote sensing areas. Existing destriping methods mainly concentrate on patch-based model representation, which ignores the heterogeneous components across the degraded MSIs and poorly utilizes the prior knowledge of the image cubes of the MSIs on high-level perception. In this article, we present a component pixel-aware MSIs destriping method. Intuitively, the image cube of the MSIs is divided into three different components (the jump, transition, and gentle components) based on the gradients of each pixel. Then, three different regularization operators are adopted adaptively for different components in a pixel-level manner. In addition, to properly depict the stripe cube of the MSIs, we exploit the nonconvex nonsmooth iteratively reweight nuclear norm. By incorporating both improved terms, our proposed method can estimate the component-variant linear representation coefficients and model the structural information of both the stripe cube and image cube. The alternating direction method of multipliers (ADMM) is adopted to solve the proposed method. Evaluating various destriping performance measurements, the algorithm proposed in this article outperforms other state-of-the-art methods under extensive experiments on synthetic and real datasets.
      PubDate: TUE, 13 JUN 2023 10:01:08 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • GSM Filter: A Learning Model of Measurement Noise Distributions for
           Bayesian Filtering

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      Authors: Dan Li;Yang Yi Xiong;An Ran Fang;Jian Qiu Zhang;
      Pages: 6387 - 6404
      Abstract: In this article, a new universal Bayesian filter with a Gaussian scale mixture (GSM) model, called the GSM filter, for adaptively learning any measurement noise distribution of a state-space system is presented. It is demonstrated that the discrete measurement noise distribution of the system takes on a Gaussian sum form when described by a GSM model. It implies that, as the Gaussian sum model does, the GSM model can also be used to approximate any measurement distribution as closely as desired, whatever the probability density function (pdf) of the measurement noise is. If its covariance is known as a priori or can be estimated, it is found that the scale parameter and matrix of the GSM model at each time step can be achieved adaptively to make the following Bayesian inference much simpler. Further analyses show that the GSM model's auxiliary random variable and the system's state posterior at each time step can be inferred by a variational Bayesian (VB) approach adaptively. At each time step, the posterior distribution of system states shown is a Gaussian whatever the measurement noise pdf of the system is. As a result, the Bayesian filtering for nonlinear systems with any measurement noise pdf can be done by our GSM filter, almost as simple as the general Gaussian filters for filtering a nonlinear Gaussian system. Both simulations and experiments are conducted to verify the effectiveness and correctness of the analytical results. The results on the performances of our algorithms fully outperforming those of the existing state-of-the-art Bayesian filters are given.
      PubDate: FRI, 12 MAY 2023 10:01:24 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Automatic Modulation Recognition of Dual-Component Radar Signals Using
           ResSwinT–SwinT Network

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      Authors: Bing Ren;Kah Chan Teh;Hongyang An;Erry Gunawan;
      Pages: 6405 - 6418
      Abstract: Automatic modulation recognition plays an important role in military and civilian applications, identifying the modulation format of received signals before signal demodulation. With the increasing complexity and density of the electromagnetic environment, the multicomponent modulation radar signal recognition against various signal-to-noise ratio (SNR) conditions has become a practical and urgent problem. In this article, we propose a dual-component modulation recognition framework, which incorporates the residual Swin transformer denoise network, Swin transformer feature extraction network (SwinT), residual-attention (RA) modulation recognition head, and SNR level classifier and achieves robust recognition performance against various SNR conditions with tolerable complexity and accuracy tradeoff. First, the time–frequency analysis is employed to transform dual-component radar signals into time–frequency images (TFIs). Then, the TFIs at various SNR levels are applied to the SwinT, which generates shallow and deep feature representations for the SNR classifier and RA-modulation recognition head, respectively. The ResSwinT is initiated to reconstruct low SNR TFIs only, which are again processed by the SwinT. Finally, the RA-modulation recognition head provides modulation format predictions. The proposed framework can identify randomly combined dual-component radar signals from 12 modulation formats, meanwhile, improving the utilization of the SwinT feature and reducing unnecessary computation of the ResSwinT. Simulation results show that the proposed scheme can obtain an exact match ratio (EMR) of larger than $\boldsymbol{97\%}$ at SNR $\boldsymbol{>\!-6}$ dB. At low SNR condition ($\boldsymbol{-12}$ dB), the ResSwinT can obtain about EMR gain of $\boldsymbol{20\%}$ and the overall framework can achieve EMR of more than $\boldsymbol{80\%}$, which outperforms other state-of-the-art methods and obtains better generalization capability.
      PubDate: THU, 18 MAY 2023 10:00:54 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • ADP-Based Attitude-Tracking Control With Prescribed Performance for
           Hypersonic Vehicles

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      Authors: Shangwei Zhao;Jingcheng Wang;Haotian Xu;Bohui Wang;
      Pages: 6419 - 6431
      Abstract: This article proposes an observer-based adaptive dynamic programming (ADP) approach to handle the optimal attitude-tracking control tasks for hypersonic vehicles with prescribed performance constraints. Herein, the biggest challenge is how to design an optimal attitude-tracking controller that could balance performance constraints and optimization capabilities in the presence of partially unknown dynamics and actuator saturation. To this end, the transformation function is designed to convert the constrained tracking error into the equivalent unconstrained one. After that, a constraint-aware neural network-based observer is proposed to estimate the future tracking error information required by the optimal controller, which avoids the requirement of the prior nonlinear dynamics knowledge, and its estimation errors are compensated through online feedback. Taking the estimated future attitude-tracking error into account, the critic-only ADP approach is designed via the value iteration to achieve the near-optimal control strategy for equivalent systems with a simplified, optimized control structure. Furthermore, a novel experience replay-driven weight updating law is provided to circumvent the conventional persistent excitation condition with an online-verification condition, and the algorithm convergence and closed-loop stability are discussed with the help of the Lyapunov technique and induction. Finally, a comprehensive simulation of an attitude-tracking control system for hypersonic vehicles is provided to verify the effectiveness and superiority of the proposed approach.
      PubDate: TUE, 16 MAY 2023 10:01:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Reference-Free Amplitude-Based WiFi Passive Sensing

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      Authors: Fabiola Colone;Francesca Filippini;Marco Di Seglio;Paul V. Brennan;Rui Du;Tony Xiao Han;
      Pages: 6432 - 6451
      Abstract: The parasitic exploitation of WiFi signals for passive sensing purposes is a topic that is attracting considerable interest in the scientific community. In an attempt at meeting the requirements for sensor compactness, easy deployment, and low cost, we resort to a noncoherent signal processing scheme that does not rely on the availability of a reference signal and relaxes the constraints on the sensor hardware implementation. Specifically, with the proposed strategy, the presence of a moving target echo is determined by detecting the amplitude modulation that it produces on the direct signal transmitted from the WiFi access point. We investigate the target discrimination capability of the resulting sensor against the competing interference background and we theoretically characterize the impact of undesired amplitude fluctuations in the received signal that are determined by causes other than the superposition of the target echo, thereby including the waveform properties. Hence, we propose different solutions to address the limitations identified, characterized by different complexities, and we investigate their advantages and drawbacks. The conceived signal processing schemes are thoroughly validated on both simulated and experimental data, collected in different operational scenarios.
      PubDate: TUE, 16 MAY 2023 10:01:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Spherical-Earth-Based Measurement Modeling for Practical OTHR Target
           Tracking

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      Authors: Zheng Wei;Zhansheng Duan;Mahendra Mallick;
      Pages: 6452 - 6468
      Abstract: In this article, we revisit the problem of modeling of over-the-horizon radar (OTHR) target tracking. The accuracies of target kinematic and measurement models are critical factors affecting tracking performance. In most of the existing literature, a flat Earth OTHR measurement model is used. This has two obvious drawbacks: 1) a state vector with components of ground range, range rate, bearing, and bearing rate cannot be used directly in common kinematic models, such as nearly constant velocity (NCV), nearly constant acceleration (NCA), nearly constant turn (NCT), and a combination of these; and 2) the curvature of earth is ignored, which affects the measurement accuracy of the OTHR. We develop a spherical-earth-based measurement model. First, the target kinematic state is modeled in a 2D tracker coordinate frame, which is suitable for common kinematic models. Second, measurement modeling is completed using the spherical earth model, which fully considers the curvature of earth among the receiver, transmitter, and target. Furthermore, the inverse mapping for track initiation is derived. Finally, the effectiveness of the developed model is demonstrated by a nonmaneuvering target moving with the NCV motion and a maneuvering target moving with a sequence of NCV, NCT, and NCA motions. The interacting multiple model algorithm is used for state estimation of the maneuvering target. The unscented Kalman filter, cubature Kalman filter, and quadrature Kalman filter are compared for state estimation.
      PubDate: TUE, 09 MAY 2023 10:02:12 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • ROSIA: Rotation-Search-Based Star Identification Algorithm

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      Authors: Chee-Kheng Chng;Álvaro Parra Bustos;Benjamin McCarthy;Tat-Jun Chin;
      Pages: 6469 - 6484
      Abstract: This article presents a rotation-search-based approach for addressing the star identification (Star-ID) problem. The proposed algorithm, ROSIA, is a heuristics-free algorithm that seeks the optimal rotation that maximally aligns the input and catalog stars in their respective coordinates. ROSIA searches the rotation space systematically with the branch-and-bound (BnB) method. Crucially affecting the runtime feasibility of ROSIA is the upper bound function that prioritizes the search space. In this article, we make a theoretical contribution by proposing a tight (provable) upper bound function that enables a 400× speed-up compared to an existing formulation. Coupling the bounding function with an efficient evaluation scheme that leverages stereographic projection and the R-tree data structure, ROSIA achieves feasible operational speed on embedded processors with state-of-the-art performances under different sources of noise.
      PubDate: THU, 01 JUN 2023 10:05:11 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Online Bayesian Meta-Learning for Cognitive Tracking Radar

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      Authors: Charles E. Thornton;Richard M. Buehrer;Anthony F. Martone;
      Pages: 6485 - 6500
      Abstract: A key component of cognitive radar is the ability to generalize or achieve consistent performance across a range of sensing environments, since aspects of the physical scene may vary over time. This presents a challenge for learning-based waveform selection approaches, since transmission policies that are effective in one scene may be highly suboptimal in another. We address this problem by strategically biasing a learning algorithm by exploiting high-level structure across tracking instances, referred to as Meta-Learning. In this work, we develop an online Meta-Learning approach for waveform-agile tracking. This approach uses information gained from previous target tracks to speed up and enhance learning in new tracking instances. This results in sample-efficient learning across a class of finite-state target channels by exploiting inherent similarity across tracking scenes, attributed to common physical elements such as target type or clutter statistics. We formulate the online waveform selection problem within the framework of Bayesian learning and provide prior-dependent performance bounds for the Meta-Learning problem using probability approximately correct Bayes theory. We present a computationally feasible metaposterior sampling algorithm and study the performance in a simulation study consisting of diverse scenes. Finally, we examine the potential performance benefits and practical challenges associated with online Meta-Learning for waveform-agile tracking.
      PubDate: FRI, 12 MAY 2023 10:01:23 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Comments on the Convex Constrained Spacecraft Reorientation

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      Authors: Yaroslav Mashtakov;Sergey Shestakov;
      Pages: 6501 - 6507
      Abstract: In this article, we investigate a class of Lyapunov-based attitude control algorithms dedicated to keep-out zone avoidance. They utilize convex representations of the allowed attitude and potential barriers that are also supposed to be convex. We show that such parameterization is not actually convex, and that suggested control generates additional asymptotically stable equilibria.
      PubDate: FRI, 19 MAY 2023 10:01:47 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Outage Probability Analysis of Rate-Splitting Multiple-Access-Based Hybrid
           Satellite–Terrestrial Relay Network With Relay Selection

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      Authors: Mehmet Can;Ibrahim Altunbas;
      Pages: 6508 - 6517
      Abstract: In this article, we investigate the outage performance of a rate-splitting multiple access (RSMA)-based hybrid satellite–terrestrial relay network with relay selection. We focus on two relay selection strategies, i.e., partial relay selection and two-stage relay selection. The satellite–terrestrial relay channels and terrestrial relay-user channels follow the shadowed Rician and Nakagami-$m$ fading, respectively. The effect of imperfect channel state information is also taken into account. The exact and asymptotic outage probability expressions are derived, and the diversity gains and coding gains are obtained. Simulation results validate the derived outage probability expressions. The power allocation optimization problem is formulated in terms of user fairness with the help of derived asymptotic outage expressions. Moreover, the RSMA-based system is compared with the nonorthogonal multiple access (NOMA)-based system. It has been shown that the RSMA-based system outperforms the NOMA-based system, especially when the number of users is high.
      PubDate: TUE, 16 MAY 2023 10:01:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Tracking of Elliptical Object With Unknown but Fixed Lengths of Axes

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      Authors: Mingkai Li;Jian Lan;X. Rong Li;
      Pages: 6518 - 6533
      Abstract: This article addresses the problem of tracking an elliptical object (e.g., a vehicle or aircraft carrier) with unknown but fixed lengths of axes. In practice, such axis lengths are usually time invariant, but the orientation and kinematics may be time varying. To model this extended object tracking (EOT) problem well, we represent the kinematics and orientation by a random vector, and represent the axis lengths by nonrandom unknown parameters. We investigate the expectation-maximization (EM) algorithm and propose an EM-based EOT approach, which utilizes the prior information about the invariant lengths and estimates the state and parameters in a unified framework. To reduce computation for real-time applications, we develop a recursive, easy to implement approach. Handy and efficient estimation of axis lengths is developed. Simulation and real-data results are presented to illustrate the effectiveness of our modeling and approach.
      PubDate: TUE, 16 MAY 2023 10:01:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Demand-Driven Beam Densification in Multibeam Satellite Communication
           Systems

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      Authors: Puneeth Jubba Honnaiah;Eva Lagunas;Symeon Chatzinotas;Jens Krause;
      Pages: 6534 - 6554
      Abstract: Traditional multibeam geostationary satellite communication systems provide broadband coverage using a regular grid of fixed spot-beams with uniform four-color frequency reuse scheme. However, user distribution is nonuniform on ground and, consequently, the demand distribution varies geographically. One potential solution to address high-demand regions is to enhance the satellite beam gain only in those areas. In this article, we propose the so-called demand driven beam densification approach, which leverages the recent advances in on-board active antenna technologies to generate a higher number of beams over high demand hot-spot areas. Increasing the number of beams result in higher beam overlapping, which needs to be carefully considered within the beam frequency planning. In this context, we propose a combination of beam densification, where the number of beams and beam placement is optimized targeting the demand satisfaction objective, followed by frequency-color coding strategy for efficient spectrum and interference management. Supporting results based on numerical simulations show the benefits of the proposed demand driven beam densification in terms of demand matching performance compared with nondensified schemes and regular densification schemes.
      PubDate: MON, 22 MAY 2023 10:03:22 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Manifold-Based Airfoil Geometric-Feature Extraction and Discrepant Data
           Fusion Learning Method

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      Authors: Yu Xiang;Liwei Hu;Guangbo Zhang;Jun Zhang;Wenyong Wang;
      Pages: 6555 - 6569
      Abstract: The geometrical shape of airfoils and the corresponding flight conditions are crucial factors for aerodynamic coefficient prediction. The obtained geometric-features of airfoils in most existing approaches (e.g., geometrical parameter extraction, polynomial description, and deep learning) are in Euclidean space. State-of-the-art studies have shown that the curves or surfaces of an airfoil form a manifold in Riemannian space. Therefore, the features extracted by existing methods are not sufficient to reflect the geometric-features of airfoils. Meanwhile, flight conditions and geometric-features are greatly discrepant with different types. The discrepancy between these two factors must be considered and evaluated to improve the aerodynamic coefficient accuracy. Motivated by the advantages of manifold theory and multitask learning (MTL), we propose a manifold-based airfoil geometric-feature extraction and discrepant data fusion learning method (MDF) to extract geometric-features of airfoils in Riemannian space (we call them manifold-features) and further fuse the manifold-features with flight conditions to predict aerodynamic coefficients. Experimental results show that our method can extract geometric-features of airfoils more accurately than existing methods, that the average mean square error (MSE) of airfoils rebuilt based on geometric-features is reduced by 41.30%, and that while keeping the same prediction accuracy level for the lift coefficient $C_{L}$, the MSE of the drag coefficient $C_{D}$ predicted by MDF is further reduced by 54.56%.
      PubDate: MON, 22 MAY 2023 10:03:22 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Partial State Feedback MRAC-Based Reconfigurable Fault-Tolerant Control of
           Drag-Free Satellite With Bounded Estimation Error

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      Authors: Xiaoyun Sun;Qiang Shen;Shufan Wu;
      Pages: 6570 - 6586
      Abstract: Aiming at the ultraprecision and ultrastable requirements of drag-free control in space detection missions, a multivariable model reference adaptive control (MRAC) scheme is proposed in this article based on partial observation state, to provide adaptive suppression of uncertain disturbances and improve detection accuracy. The MRAC scheme utilizes model output matching with partial state containing uniform and bounded observation error, and estimates the unknown state parameters through the adaptive law and high-frequency gain decomposition. In response to the actuator bias fault of the drag-free satellite, a set of state error iterative convergence sequence in the actuator loop is established based on the sequence Lyapunov analysis, which is further introduced into the reconfiguration control input to achieve the fault-tolerant target without changing the nominal controller. Through the Lyapunov stability analysis, the convergence of the closed-loop system states in the presence of actuator faults and observation errors is obtained. Numerical simulation results for a 6-DOF drag-free control system demonstrate the effectiveness of the proposed MRAC-based reconfigurable fault-tolerant control scheme.
      PubDate: TUE, 16 MAY 2023 10:01:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Dynamics and Control for Transverse Formation by a Distributed Drag Sail

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      Authors: Jixin Ding;Xiaoyuo Zuo;Ming Xu;Lin Chen;Yifan Wang;
      Pages: 6587 - 6598
      Abstract: The constant effective vertical measurement baseline provided by the transverse formation satellites in low orbit has a critical application prospect for ground and high-orbit target detection. However, traditional formation satellites decay owing to atmospheric perturbation, and their effective baseline is periodically time-varying. A distributed drag sail (DDS) with several louvered subsails is promoted based on the free molecular flow theory, which allows for high maneuverability in satellite-tether-sail (STS) transverse formation flying. Moreover, the fuzzy proportion differentiation (PD) control is introduced into the attitude-orbit model, which attains the time-dependent PD coefficients based on the geometrical configuration, to handle coupled attitude-orbit of the DDS. The aerodynamic force vectors are distributed geometrically in a bounded two-dimensional area with a pentagonal shape, through different twist combinations of subsails. The size of the distributed area depends on the number of subsails, the length, and the width of DDS. Finally, it is achieved that the steering engines of DDS can work effectively in appropriate frequencies during the stable formation configuration. Binocular detection with a constant vertical measurement baseline can be provided by DDS to gain situational awareness of space targets in practical scenarios such as remote sensing applications.
      PubDate: MON, 15 MAY 2023 10:02:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Analog Maximum Peak Power Tracking Techniques for Small Satellites

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      Authors: Cristian Torres;Ausiàs Garrigós;José M. Blanes;Pablo Casado;David Marroquí;
      Pages: 6599 - 6611
      Abstract: This work describes the implementation of three maximum peak power tracking methods devised for small satellites. The three methods are the analog oscillating maximum power point tracking, the analog global maximum peak power tracking, and the analog global maximum output power tracking. An interplanetary mission (Mars–Asteroid belt) with complex power–voltage solar array characteristics, including several local maximum power points, is considered to evaluate each peak power tracking technique. The three peak power tracking techniques have been integrated into an unregulated battery bus topology using synchronous buck converters as solar array regulators. High-reliability design is achieved using analog electronic parts with space-qualified counterparts. Each peak power tracking method has been optimized individually for the best performance and then compared with the others. The experimental validation suggests that the preferred method strongly depends on the expected power–voltage solar array characteristics and mission parameters.
      PubDate: MON, 15 MAY 2023 10:02:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Feedforward Quadrotor Disturbance Rejection Method for Visually
           Identified Gust Sources Based on Transfer Reinforcement Learning

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      Authors: Fulin Song;Zhan Li;Xinghu Yu;
      Pages: 6612 - 6623
      Abstract: With the rapid development of urbanization, quadrotors are flying more and more frequently between high-density buildings. In urban areas, there are many gust sources with obvious visual characteristics, such as ventilation outlet of buildings or subways, outdoor unit of heating ventilating and air conditioning, or other artificial gust sources, which can cause sudden gust disturbances to the passing quadrotors. Aiming at reducing the effect of visually identified gust disturbances, this article presents a quadrotor feedforward compensator framework based on transfer reinforcement learning (TRL). In this structure, the discriminative model prediction (DiMP) algorithm is used to extract the visual features of the gust source and achieve tracking of such sources. Then, the TRL algorithm is used to transfer the obtained tracking network to a compensation network, which completes a DiMP-TRL structure. The output of the trained DiMP-TRL compensation network is added to the quadrotor controller as a feedforward part, which will help generate preventive action when encountering the identified gust disturbance. Only a front-facing camera is needed to collect gust information; no additional anemometer or accurate wind model is required. The ultimately uniformly bounding of the quadrotor system can be guaranteed using the Lyapunov stability criteria. The proposed method is compared with a cascade proportional–integral–differential controller and a novel feedback-compensation-based method called dual command filter in a realistic simulation environment built by a physics engine. Two sets of results demonstrate that the proposed method can reject gust disturbance more efficiently.
      PubDate: MON, 15 MAY 2023 10:02:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Prescribed-Time Fault-Tolerant Control for Fully Actuated Heterogeneous
           Multiagent Systems: A Hierarchical Design Approach

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      Authors: Yonghao Ma;Ke Zhang;Bin Jiang;
      Pages: 6624 - 6636
      Abstract: The formation control of heterogeneous multiagent systems that are subjected to actuator faults is addressed in this work. To prevent the spread of faults among agents, a hierarchical design approach is utilized. In the upper layer, distributed prescribed-time observers are presented to estimate the leader's trajectory for followers on directed graphs. In the lower layer, adaptive fault-tolerant controllers are provided for the heterogeneous multiagent systems subjected to partial effectiveness loss faults based on the fully actuated system model. Contrary to the results of fault-tolerant control that are already in use, the proposed method enables formation errors to zero within a predetermined time that can be set freely. What is more, the estimated errors of efficiency factors are bounded. Finally, simulation results are provided to show the suggested protocol's availability.
      PubDate: MON, 05 JUN 2023 10:02:34 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Learning-Inspired Strategy to Design Binary Sequences With Good
           Correlation Properties: SISO and MIMO Radar Systems

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      Authors: Omid Rezaei;Mahdi Ahmadi;Mohammad Mahdi Naghsh;Augusto Aubry;Mohammad Mahdi Nayebi;Antonio De Maio;
      Pages: 6637 - 6657
      Abstract: In this article, the design of binary sequences exhibiting low values of aperiodic/periodic correlation functions, in terms of integrated sidelobe level (ISL), is pursued via a learning-inspired method. Specifically, the synthesis of either a single or a burst of codes is addressed, with reference to both single-input single-output (SISO) and multiple-input multiple-output (MIMO) radar systems. Two optimization machines, referred to as two- and single-layer binary sequence correlation networks, able to learn actions to design binary sequences with small ISL/complementary ISL for SISO and MIMO systems are proposed. These two networks differ in terms of the capability to synthesize low-correlation-zone sequences and computational cost. Numerical experiments show that proposed techniques can outperform state-of-the-art algorithms for the design of binary sequences and complementary sets of sequences in terms of ISL and, interestingly, of peak sidelobe level.
      PubDate: MON, 29 MAY 2023 10:03:19 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Flexible Final-Time Stochastic Differential Dynamic Programming for
           Autonomous Vehicle Trajectory Optimization

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      Authors: Xin Sun;Runqi Chai;Senchun Chai;Baihai Zhang;Antonios Tsourdos;
      Pages: 6658 - 6669
      Abstract: In this article, the problem of autonomous vehicle trajectory optimization with flexible final time is concerned under the consideration of stochastic disturbances. Stochastic differential dynamic programming (SDDP) has been widely applied to address this type of problem due to its fast convergence and capability to handle model errors. Typically, traditional SDDP is designed with a deterministic final time, which is mainly assigned based on expert experiences. However, this may limit the implementation of this approach. To deal with this issue, we define the final time as a new optimal variable and propose an enhanced version of SDDP, named flexible final-time SDDP. The stochastic dynamic system is then characterized as a deterministic one with random state perturbances. An unscented transform approach is utilized to cope with the perturbed expected values. To verify the effectiveness of the proposed approach, a 3-D missile trajectory optimization problem is tested as an example. The simulation results show that the proposed method is able to address the stochastic trajectory optimization problem and can provide stronger robustness compared to other algorithms.
      PubDate: MON, 29 MAY 2023 10:03:19 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Time-Optimal Path Tracking for Dual-Arm Free-Floating Space Manipulator
           System Using Convex Programming

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      Authors: Quan An;Yao Zhang;Quan Hu;Mou Li;Jinjian Li;Anyuan Mao;
      Pages: 6670 - 6682
      Abstract: In this article, the time-optimal path-tracking problem of the dual-arm free-floating space manipulator (FFSM) system is solved by using convex programming. First, we construct a continuous nonlinear optimization problem that considers system dynamics and constraints along the path, including torque, velocity, and acceleration. Subsequently, the original optimization problem is transformed into a convex optimal control problem by using scalar path coordinate and nonlinear variables. To characterize the problem in a more manageable form, we apply the direct transcription method and discretize the problem into a sparse convex optimization program. The numerical results demonstrate that the proposed approach efficiently determines the trajectory of the FFSM along the preset path. Meanwhile, the base's velocity can be limited to a small region, which is an essential requirement for ensuring the stability of the satellite. Furthermore, an experiment is conducted using GLUON robots. The tracking error between the actual and preset pose of the target verifies the feasibility of the proposed method.
      PubDate: TUE, 16 MAY 2023 10:01:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Labeled Multi-Bernoulli Filter Based Multiple Resolvable Group Targets
           Tracking With Leader–Follower Model

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      Authors: Guchong Li;Gang Li;You He;
      Pages: 6683 - 6694
      Abstract: This article addresses the multiple resolvable group targets tracking (MRGTT) problem, wherein the leader–follower (LF) model is adopted to evolve the group structure. Since there is an interaction between the destination and each leader, such that an attraction force of the destination information on the leader needs to be considered. To cope with the time-varying number of targets/groups and target-measurement association uncertainty, the so-called labeled multi-Bernoulli (LMB) filter is regarded as the filtering tool and combined with the LF model, named LF-LMB. Totally speaking, the implementation of the proposed LF-LMB filter consists of three steps. First, the stochastic differential equation (SDE) of the state is used to, respectively, characterize the motion models of the leader and the follower. Then, the state transition equations (STEs) for the leader and the follower are derived. Lastly, the derived STEs are used to modify the prediction process of the standard LMB filter. Simulation experiments are carried out to demonstrate the superiority (e.g., optimal subpattern assignment (OSPA) and OSPA$^{(2)}$ errors are reduced by at least 15$\%$ compared to the standard LMB filter) and robustness of the proposed approach.
      PubDate: TUE, 16 MAY 2023 10:01:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Finite-Time Tracking Control With Fast Reaching Condition for Disturbed
           

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      Authors: Zhaoyang Zuo;Navid Vafamand;Saleh Mobayen;Tomislav Dragičević;
      Pages: 6695 - 6704
      Abstract: Future more electric aircraft (MEA) can be seen as a direct current microgrid (dc MG). While dc MG can generally achieve safe, efficient, and reliable operation of the system, constant power loads (CPLs), unknown resistive loads, dc source voltage variations, and disturbance inputs might degrade the overall stability and performance if they are not compensated in the control design procedure. In this article, a fast-reaching condition-based sliding mode control (SMC) is proposed for the dc MG. The fast-reaching law contains two terms that improve the transient performance by forcing the system states to reach a sliding surface with two different rates. This attribute enhances the speed of convergence of the states to the sliding surface compared to conventional SMC. Additionally, the suggested method is resilient to system parameter uncertainties, which is indispensable in practice. The proposed controller is implemented to a dc MG that is supplied by an uncertain dc source and feeds CPLs and resistive load with time-varying power and resistance. OPAL RT results illustrate the robustness and fast convergence of the planned SMC controller.
      PubDate: TUE, 16 MAY 2023 10:01:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Adaptive Fault-Tolerant Formation Control for Heterogeneous UAVs-UGVs
           Systems With Multiple Actuator Faults

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      Authors: Yajie Ma;Bin Jiang;Juan Wang;Jianye Gong;
      Pages: 6705 - 6716
      Abstract: This article investigates the fault-tolerant formation control problem for heterogeneous multiagent systems (MASs) consisting of quad-rotor unmanned aerial vehicles (UAVs) and two-wheel driven unmanned ground vehicles (UGVs) in the presence of multiple actuator faults. The heterogeneous dynamic characteristics and the uncertainties of the control gain matrix generated by actuator faults, especially the sudden changes to system structure due to finite sequential faults, increase the difficulty of the formation control design. The dynamic models of UAVs and UGVs are first transformed to obtain the $XOY$ 2-D position formation systems of UAVs-UGVs and the $Z$-axis altitude system of UAVs. Then, a distributed adaptive direct fault compensation control strategy is designed for the position system of the UAVs-UGVs and the $Z$-axis altitude subsystems of UAVs, which can guarantee the expected formation structure under the influence of finite sequential faults. A simulation study based on the UAVs-UGVs cooperative systems is adopted to demonstrate the validity of the proposed fault compensation strategy.
      PubDate: THU, 18 MAY 2023 10:00:54 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Time-Varying Formation Planning and Scaling Control for Tethered Space Net
           Robot

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      Authors: Ya Liu;Zhuochen Ma;Fan Zhang;Panfeng Huang;
      Pages: 6717 - 6728
      Abstract: The capture for space debris by tethered space net robot (TSNR) is formulized as a time-varying formation tracking problem with scaling size. To ensure a successful capture, the contact dynamics, capture configuration optimization, and scaling formation tracking control are investigated in this article. Different from existing research, a more realistic space debris with a complex shape is involved to gain understanding of contact dynamics. Moreover, the design of size scaling wrap configuration of the TSNR during capture is converted to an approximate path planning problem solved by sequential convex programming. In addition, a formation scaling factor is introduced to regulate the opening area of the TSNR to guarantee a successful net capture. Considering the case that the desired size scaling factor of the formation is known only by one maneuvering unit, an observer-based time-varying formation tracking control algorithm is proposed. Finally, the effectiveness of the planning and control strategy for capturing space debris through TSNR are demonstrated.
      PubDate: THU, 25 MAY 2023 10:01:47 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • An Efficient Approach for Coherent Integration Detection of High-Speed
           Maneuvering Targets With Arbitrary-Order Doppler Frequency Migrations

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      Authors: Jun Wan;Xiaohua Kang;Xiaoheng Tan;Dong Li;Zhanye Chen;Hongqing Liu;
      Pages: 6729 - 6748
      Abstract: In radar detection applications, high-speed maneuvering targets (HSMTs) are commonly encountered. Given the range cell migration (RCM) and arbitrary-order Doppler frequency migrations (DFMs) induced by the complex relative motions between the HSMT and radar, they usually present a great challenge for target detection. In addition, the complex Doppler ambiguity will further aggravate the difficulty in focusing on HSMT. To address these issues, an efficient approach for coherent integration detection of HSMTs is proposed. First, inspired by the symmetrical feature of range frequency, a new range frequency reversal transform is introduced to correct complex arbitrary-order DFMs simultaneously. After that, by exploiting the advantage of range–azimuth joint processing, a modified two-dimensional scaled Fourier transform is developed to remove the residual RCM, and a well-focused result is thus achieved. Furthermore, a discrimination procedure is proposed to remove the spurious peak for cross terms of targets with the same parameters. The proposed method has an extremely low computational cost and does not need to know the target motion order a priori because the operations of multidimensional search and parameter estimation are avoided. It also handles complex arbitrary-order DFMs, blind speed sidelobe, and complex Doppler center blur and spectrum ambiguity. The experiments using real data also validate the performance of the proposed method.
      PubDate: TUE, 16 MAY 2023 10:01:26 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Open-Metric for Unknown Signal Inference

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      Authors: Xinyu Li;Lutao Liu;Yu Zheng;
      Pages: 6749 - 6758
      Abstract: The closed-set assumption that only known classes can be classified during testing is usually adopted in traditional signal recognition missions. It has been thoroughly explored and made huge progress in the past decade. However, it cannot cope with the unknown categories coming from the changing environment. As waveform diversity technology evolves by leaps and bounds, the existence of novel unknown classes is inevitable in the open-world environment, imposing a huge challenge to the noncooperative electronic support system. To further satisfy the demands of modern electronic intelligence systems and sense new threats, it is urgent to find an approach that can tackle unknown signals. An open-metric strategy is proposed in this article to deduce the unknown classes from the intercepted signals. By virtue of metric learning methods, a plain backbone can be trained to offer more discriminative representations and a consistent proxy for each class. According to extreme value analysis, information about known classes can be converted into an estimated score of unknown classes, creating an open decision layer. The investigated open-metric approach demonstrates its superiority in unknown signal inference tasks, facilitating improvements in the intelligence and smartness of the electronic reconnaissance agents to cope with future cognitive electronic warfare.
      PubDate: THU, 18 MAY 2023 10:00:54 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • $\ell _{0}$-Norm Minimization-Based Robust Matrix Completion Approach for
           MIMO Radar Target Localization

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      Authors: Zhaofeng Liu;Xiao Peng Li;Hing Cheung So;
      Pages: 6759 - 6770
      Abstract: In this article, we propose a robust matrix completion approach based on $\ell _{0}$-norm minimization for target localization in sub-Nyquist sampled multiple-input–multiple-output (MIMO) radar. Owing to the low-rank property of the noise-free MIMO radar transmit matrix, our approach is able to recover the missing data and resist impulsive noise from the receive matrix. We adopt proximal block coordinate descent and adaptive penalty parameter adjustment by complex Laplacian kernel and normalized median absolute deviation. We analyze the resultant algorithm convergence and computational complexity, and demonstrate through simulations that it outperforms existing methods in terms of pseudospectrum, mean square error, and target detection probability in non-Gaussian impulsive noise, even for the full sampling schemes. While in the presence of Gaussian noise, our approach performs comparably with other sub-Nyquist methods.
      PubDate: MON, 29 MAY 2023 10:03:19 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Self-Calibration of a Network of Radar Sensors for Autonomous Robots

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      Authors: Timo Grebner;Vinzenz Janoudi;Pirmin Schoeder;Christian Waldschmidt;
      Pages: 6771 - 6781
      Abstract: Radar sensor networks are today widely used in the field of autonomous driving and for generating high-precision images of the environment. The accuracy of the environmental representation depends to a large extent on the accurate knowledge of the sensor's mounting orientation. Both the relative orientation of the sensors to each other and the relative sensor orientation in relation to the vehicle coordinate system are determining factors. For the first time, the orientation estimation of the radar sensors of a network is possible exclusively on the basis of radar target lists without additional localization and orientation devices, such as an inertial measurement unit or global navigation satellite systems. In this work, two algorithms for determining the orientation of incoherently networked radar sensors with respect to the vehicle coordinate system and with respect to each other are derived and characterized. With the presented algorithms, orientation accuracies up to $0.25 \mathrm{^{\circ }}$ are achieved. Furthermore, the algorithms do not impose any requirements on the positioning or the orientation of the radar sensors, such as overlapping field of views or the detection of identical targets. The presented algorithms are applicable to arbitrary driving trajectories as well as for point targets and extended targets which enables the use in regular road traffic.
      PubDate: THU, 18 MAY 2023 10:00:54 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Piecewise Trajectory and Angle Constraint-Based Fractional-Order Sliding
           Mode Control

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      Authors: Junfang Fan;Shiwei Chen;Wei Wang;Yi Ji;Ning Liu;
      Pages: 6782 - 6797
      Abstract: This article proposes an approach for piecewise trajectory planning and fractional-order sliding-mode guidance of a two-stage launched miniature munition with the laser beam riding steered guidance technology. In the proposed approach, first, the polynomial description is used as an expected trajectory. Then consider the random external disturbances/parametric uncertainties and other factors deteriorate the tracking control performances of the miniature munition. To reduce the chattering caused by modeling errors and external disturbances and achieve fast speed and high-accuracy tracking performances for miniature munition, based on adaptive multipower reaching strategy, an integrated guidance and control (IGC) method with the fractional-order sliding-mode guidance law (FOSMGL) is introduced. The designed fractional-order sliding surfaces ultimately and successively allow both the trajectory and the attitude angle converge to expected value in a finite time. In addition, considering the case of a moving target, a feedforward compensation mechanism, which decreases the lag of the guidance system, is imposed using an additional compensated term obtained from the attitude angle error. The convergence and reachability of the proposed algorithm in finite-time are analyzed using the Lyapunov function method. The validity and practicability of the proposed method are verified by a numerical example.
      PubDate: MON, 05 JUN 2023 10:02:34 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Robust and Statistically Efficient Maximum-Likelihood Method for DOA
           Estimation Using Sparse Linear Arrays

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      Authors: Zai Yang;Xinyao Chen;Xunmeng Wu;
      Pages: 6798 - 6812
      Abstract: The recent trend of research on direction-of-arrival estimation is to localize more uncorrelated sources than sensors by using a proper sparse linear array (SLA) at the cost of robustness to source correlations even in the regime of less sources than sensors. This article is devoted to proposing one algorithm that can simultaneously tackle two challenging scenarios: 1) more uncorrelated sources than sensors and 2) highly correlated or coherent sources. In order to statistically efficiently localize a maximal number of uncorrelated sources, we use the stochastic maximum likelihood (SML) criterion and propose an effective algorithm based on elegant problem reformulations and the alternating direction method of multipliers (ADMM). Moreover, we prove that the SML is robust to source correlations under mild conditions, though it is derived under the assumption of uncorrelated sources. The proposed algorithm is usable for arbitrary SLAs (e.g., minimum redundancy arrays, nested arrays, and coprime arrays) and is named as maximum-likelihood estimation via sequential ADMM (MESA). Extensive numerical results are provided that collaborate our analysis and demonstrate the statistical efficiency and robustness of MESA against state-of-the-art algorithms. Our results also imply that it is possible to localize more sources than sensors in the presence of high source correlations.
      PubDate: FRI, 02 JUN 2023 10:02:45 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Low-Angle Target Tracking in Sea Surface Multipath Using Convolutional
           Neural Networks

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      Authors: Alexander Karlsson;Magnus Jansson;Mikael Hämäläinen;
      Pages: 6813 - 6831
      Abstract: Multipath interference while tracking sea-skimming targets can significantly distort the estimated height of the target. If accounted for however, this interference can be used to obtain more accurate estimates. In this study, we accomplish this with a convolutional neural network (CNN) used as a parameter estimator. The performance of this network is compared with maximum likelihood and least-squares methods. We found that the CNN performs well in comparison to these methods with only a fraction of the computations.
      PubDate: FRI, 02 JUN 2023 10:02:45 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Effect of Target Motion on Equivalent Transmit Beamforming for the
           Frequency Diverse Array

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      Authors: Abeer Nasir Chaudhry;Hasan Saeed Mir;Kainam Thomas Wong;
      Pages: 6832 - 6840
      Abstract: Conventional antenna-array beamforming along a selected direction cannot discriminate between different far-field subranges. To select a particular subrange in the far-field, a distance-specific multiantenna transmission architecture known as the frequency diverse array along with a matching receiver architecture known as equivalent transmit beamforming (ETB) has been recently proposed. However, these breakthroughs have not explicitly addressed the effects of target motion, which is ubiquitous in radar applications. These effects are analyzed in this article, leading to the conception of a range-direction-Doppler version of the “direction cosine” which is used to develop analytical formulas for the ETB beampattern characteristics in terms of the ETB system parameters along with insights for system design.
      PubDate: FRI, 02 JUN 2023 10:02:45 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Cerebellar Model Articulation Neural Network-Based Distributed Fault
           Tolerant Tracking Control With Obstacle Avoidance for Fixed-Wing UAVs

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      Authors: Moshu Qian;Zhu Wu;Bin Jiang;
      Pages: 6841 - 6852
      Abstract: In this article, the distributed fault-tolerant tracking control and obstacle avoidance problem is investigated for multiple unmanned aerial vehicles (UAVs) considering lumped disturbances and communication link faults. First, a cerebellar model articulation neural network s introduced to estimate the lumped disturbances. Meanwhile, the distributed virtual leader state observers are used to address unknown communication faults. Then, a distributed nonsingular fast terminal sliding mode formation controller is implemented to track desired trajectory, and a virtual-agent artificial potential function (APF) is designed to accomplish obstacle avoidance. Furthermore, the stability of the closed loop formation control systems with obstacle avoidance is proved using the graph theory and the Lyapunov theory. Finally, simulation results of three fixed-wing UAVs are given to show the effectiveness and good performance of the proposed scheme.
      PubDate: MON, 05 JUN 2023 10:02:34 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Two-Dimensional Direction Finding Based on Cylindrical Nested Conformal
           Array

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      Authors: Mingcheng Fu;Zhi Zheng;Wen-Qin Wang;
      Pages: 6853 - 6862
      Abstract: Recently, nested conformal arrays have attracted considerable interest due to their ability to provide increased array aperture and enhanced degrees of freedom compared to uniform conformal arrays. In this article, we address the problem of 2-D direction finding using the cylindrical nested conformal array (CNCA). First of all, we derive the coarray signal model of the CNCA and utilize different coarray signal components to construct multiple Toeplitz matrices. Based on the constructed Toeplitz matrices, we further develop two direction-finding algorithms to achieve the decoupled estimation of the 2-D direction. Unlike the exiting approaches, the proposed two algorithms exploit more coarray output components, and hence, they have better direction-finding performance. Moreover, our algorithms can be applied to a larger number of nested subarrays in the CNCA. Numerical results demonstrate the superiority of the proposed algorithms in comparison to the exiting methods.
      PubDate: WED, 24 MAY 2023 10:02:05 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Maintenance Strategy for Elliptical Orbit Satellite With Air-Breathing
           Electric Propulsion

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      Authors: Xiaoyu Zuo;Ming Xu;Meili Huang;Ming Li;Na Peng;
      Pages: 6863 - 6877
      Abstract: Owing to the strong perturbation caused by atmospheric drag, it is inevitable to consume a significant amount of fuel for the orbital maintenance of ultralow Earth orbit (ULEO) satellites. Air-breathing electric propulsion (ABEP) has emerged at the right time, which makes use of the atmosphere to provide propulsion. Aiming to adopt an elliptical orbit, an ABEP-based satellite can collect the atmosphere near its perigee, which can be applied for propulsion to maintain the orbit shape. However, to guarantee propulsive efficiency, the thrusters should not work during the intake process, i.e., near the perigee. This causes difficulties in orbital control methods because propulsion near the perigee is the most effective for simultaneous increasing the semimajor axis and eccentricity. To overcome this problem, this article proposes the “in-orbit balance” strategy, wherein the orbit maintenance can be achieved in closed orbits. Some of these orbits are used for air intake, while others are applied for propulsion near perigee. Considering the requirements regarding working gas, electric power, maneuverability, and temperature, the constraints are analyzed for systematic parameter design. Using equivalent treatments, feasible satellite layout schemes can be searched for, and a constrained optimization problem is constructed to determine the solutions that satisfy the specific engineering requirements. Furthermore, optimal control for a continuous system is applied to allocate the thrust. The simulations verified the efficiency of the parameter design and confirmed that the ULEO can be controlled using the proposed strategy without resulting in extra fuel consumption.
      PubDate: MON, 05 JUN 2023 10:02:34 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Ionospheric Scintillation Mitigation With Kalman PLLs Employing Radial
           Basis Function Networks

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      Authors: Rafael Anderson Martins Lopes;Felix Antreich;Friederike Fohlmeister;Martin Kriegel;Helio Koiti Kuga;
      Pages: 6878 - 6893
      Abstract: We investigate two adaptive Kalman phase-locked loop (PLL) structures for ionospheric scintillation mitigation for global navigation satellite systems receivers, employing radial basis function (RBF) networks to model the scintillation phase and amplitude, instead of the typically employed autoregressive (AR) models. In the first structure, the Kalman filter innovations are computed by the arctangent phase discriminator, and the state estimates are directly employed in the carrier replica generation. In the second structure, the Kalman filter measurements are the prompt correlator outputs, and the error states are computed and used by a state feedback controller to provide a control signal to drive the carrier replica generation. The nonlinear RBFs provide more flexibility to capture nonlinear dynamics evolving with time, possibly present in the scintillation phase and amplitude. The weights of the RBF networks and the covariance matrices of the process and measurement noise of the Kalman filters are estimated online in the adaptive Kalman PLL structures. Simulations with synthetic severe scintillation data show the capability of the proposed Kalman PLLs to improve robustness to scintillation effects in carrier synchronization, with performance similar to the corresponding structures employing AR scintillation models. Simulations using recorded scintillation data collected by a commercial receiver highlight the learning and generalization capability of the RBF networks to cope with evolving scintillation characteristics over time with possibly nonlinear effects. The Kalman PLL structures employing the RBF networks present reduced errors compared with the structures using AR models.
      PubDate: TUE, 30 MAY 2023 10:01:23 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • A Collaborative Relay Tracking Method Based on Information Fusion for UAVs

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      Authors: Yongxiang He;Hongwu Guo;Xudong Li;Zihao Lu;Xuanying Li;
      Pages: 6894 - 6906
      Abstract: In this article, a collaborative relay tracking method based on information fusion is proposed for unmanned aerial vehicles (UAVs). Target matching is performed by combining the target's spatiotemporal information with its appearance and other auxiliary cues. Only the motion information and a compressed image of the target are used for target handover. Specifically, to determine the spatiotemporal transfer probability of the target after entering the blind area of visual field, this article proposes a spatiotemporal correlation model construction method to predict the spatiotemporal similarity. Besides, Siam-LGNet is proposed in this article for target appearance matching to overcome the effects of various view angles, rotations, scale changes, and other factors on the images. This effectively ensures the accuracy of target matching and lowers the model complexity. In addition, similarities obtained from multiple cues are fused by D–S evidence theory to compensate for the uncertainty of single cue. Finally, the effectiveness of the proposed method is verified in simulation experiments and physical experiments. This article fills a gap in the field of UAVs relay tracking. The proposed scheme is scalable and lays the foundation for subsequent research on UAVs relay tracking problems.
      PubDate: MON, 05 JUN 2023 10:02:34 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Exploiting the Properties of Reciprocal Filter in Low-Complexity OFDM
           Radar Signal Processing Architectures

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      Authors: Javier Trujillo Rodriguez;Giovanni Paolo Blasone;Fabiola Colone;Pierfrancesco Lombardo;
      Pages: 6907 - 6922
      Abstract: This article explores signal processing architectures for disturbance cancellation and range-Doppler map evaluation in orthogonal frequency division multiplexing (OFDM) radar. The signal processing chain of an OFDM radar typically encompasses a disturbance cancellation stage followed by the range-Doppler map evaluation, which can be in turn decomposed into a range compression stage performed at OFDM symbol level and a Doppler processing across symbols. In this article, we use a reciprocal filter (RF) to perform the range compression and we deepen the understanding of the RF properties with particular reference to their impact on the other processing stages of the signal processing scheme, above all disturbance cancellation. By exploiting this understanding, we show that it is possible to create synergies between different processing stages, even swapping their order, with the aim to improve the performance of the system while keeping limited its complexity. Thanks to this strategy, alternative versions of existing disturbance cancellation algorithms can be considered that would not be feasible in conventional architectures. Moreover, this article makes it possible to include within the same interpretative framework approaches that seem to be very distant from each other in terms of processing techniques thus allowing their throughout comparison both in terms of target detection performance and in terms of computational complexity. The performance of different solutions is investigated and compared against simulated and experimental data for the case of an OFDM radar that parasitically exploits DVB-T signals of opportunity.
      PubDate: WED, 07 JUN 2023 10:01:20 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Memory Management Unit for Hardware-Assisted Dynamic Relocation in
           On-Board Satellite Systems

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      Authors: Borja Losa;Pablo Parra;Antonio Da Silva;Óscar R. Polo;Juan Ignacio García Tejedor;Agustín Martínez;Jonatan Sánchez;Sebastián Sánchez;David Guzmán;
      Pages: 6923 - 6939
      Abstract: Satellite on-board systems spend their lives in hostile environments, where radiation can cause critical hardware failures. One of the most radiation-sensitive elements is memory. The so-called single event effects (SEEs) can corrupt or even irretrievably damage the cells that store the data and program instructions. When one of these cells is corrupted, the program must not use it again during execution. In order to avoid rebuilding and uploading the code, a memory management unit can be used to transparently relocate the program to an error-free memory region. This article presents the design and implementation of a memory management unit that allows the dynamic relocation of on-board software. This unit provides a hardware mechanism that allows the automatic relocation of sections of code or data at run-time, only requiring software intervention for initialization and configuration. The unit has been implemented on the LEON architecture, a reference for the European Space Agency (ESA) missions. The proposed solution has been validated using the boot and application software (ASW) of the instrument control unit of the Energetic Particle Detector of the Solar Orbiter Mission as a base. Processor synthesis on different FPGAs has shown resource usage and power consumption similar to that of a conventional memory management unit. The results vary between $\pm$ 1–15% in resource usage and $\pm$ 1–7% in power consumption, depending on the number of inputs assigned to the unit and the FPGA used. When comparing performance, both the proposed and conventional memory management units show the same results.
      PubDate: THU, 08 JUN 2023 10:01:59 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Joint Generalized Inner Product Method for Main Lobe Jamming Suppression
           in Distributed Array Radar

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      Authors: Weiming Pu;Zhennan Liang;Jianxin Wu;Quanhua Liu;
      Pages: 6940 - 6953
      Abstract: Modern radar detection faces an increasingly complex electromagnetic environment, and strong clutter and strong jamming have severely restricted radar performance. This article proposes a robust jamming suppression method that is based on the joint generalized inner product (JGIP) method in a distributed array radar, which utilizes the characteristics of the extremely high angular resolution of the distributed array radar to effectively suppress main lobe jamming. Thus, the advantages of flexible aperture synthesis of distributed array radar are investigated to design a JGIP to detect targets in a cluttered environment. This method is not sensitive to the various nonideal factors that exist in distributed array radar engineering practice and is highly robust. Simulation and actual measured data verify the correctness and effectiveness of the proposed method.
      PubDate: MON, 29 MAY 2023 10:03:19 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Range-Ambiguous Sea Clutter Suppression for Multichannel Spaceborne Radar
           Applications Via Alternating APC Processing

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      Authors: Penghui Huang;Hao Yang;Zihao Zou;Xiang-Gen Xia;Guisheng Liao;Yuhong Zhang;
      Pages: 6954 - 6970
      Abstract: In this article, a novel range ambiguous cluter suppression method, i.e., alternating azitmuth phase coding (AAPC), based on azimuth phase coding (APC) is proposed, where the blind areas of target detection are solved. Based on the motion model of spaceborne radar (SBR) system, the sea clutter motion property is analyzed, and a multichannel sea clutter signal model with the consideration of range ambiguity is established. After that, the novel method AAPC is proposed. Finally, a time-domain sliding window projection method with the consideration of clutter internal motions is proposed to realize the sea clutter rejection. Compared with the current range-ambiguous sea clutter suppression algorithms, the improvements of this method are: 1) the range-ambiguous clutter can be separated and most of the clutter components in the main-lobe region can be rejected; and 2) the repetitive detection blind regions can be avoided by applying the proposed AAPC technique. Simulation results are presented to validate the feasibility of the proposed algorithm.
      PubDate: FRI, 02 JUN 2023 10:02:45 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • CFAR Based NOMP for Line Spectral Estimation and Detection

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      Authors: Menghuai Xu;Jiang Zhu;Jun Fang;Ning Zhang;Zhiwei Xu;
      Pages: 6971 - 6990
      Abstract: The line spectrum estimation and detection problem are considered in this article. We propose a constant false alarm rate (CFAR)-based Newtonized OMP (NOMP-CFAR) method, which can maintain a desired false alarm rate without the knowledge of the noise variance. The NOMP-CFAR consists of two steps, namely, an initialization step and a detection step. In the initialization step, NOMP is employed to obtain candidate sinusoidal components. In the detection step, CFAR detector is applied to detect each candidate frequency, and provides the “soft” thresholds. After removing the most unlikely target, the Newton refinements are used to refine the remaining parameters. The relationship between the false alarm rate and the required threshold is established. Compared with NOMP, NOMP-CFAR has only 1 dB performance loss in additive white Gaussian noise scenario with false alarm probability $10^{-2}$ and detection probability 0.8 without knowledge of noise variance. For varied noise variance scenario, NOMP-CFAR still preserves its CFAR property, while NOMP violates the CFAR. Besides, real experiments are also conducted to demonstrate the detection performance of NOMP-CFAR, compared to CFAR and NOMP.
      PubDate: MON, 05 JUN 2023 10:02:34 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Internet of Underwater Things Infrastructure: A Shared Underwater Acoustic
           

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      Authors: Zhengliang Zhu;Yuehai Zhou;Rong Wang;Feng Tong;
      Pages: 6991 - 7003
      Abstract: The advancement of underwater acoustic communication (UAC) holds great promise for the Internet of Underwater Things (IoUT). However, a critical challenge to the development of UAC technology is the difficulty of obtaining sea trial data, such difficulty makes the emerging algorithms inconvenient to evaluate. In this article, we propose a shared UAC layer (SUACL) to collect underwater acoustic signals, the proposed SUACL is founded on the IoUT, and users are allowed to remotely access the system to arrange underwater acoustic data transmission and reception via Internet. The SUACL system aims to enable the remote operation of communication units that are deployed in designated sea areas. The merit of the proposed SUACL is that the sea trial data can be obtained without risky and high expense, and the emerging underwater acoustic technologies can be evaluated in time. The SUACL is composed of the nonunderwater part, which includes the application layer and fusion layer, and the above/underwater part including the communication layer on which we focus. We provide a detailed description of the communication layer of the proposed SUACL system, such as the preamplifier with reconfigurable gain, the analog-to-digital converter with reconfigurable sampling rates, the reconfigurable transducers with different frequency bands, and the reconfigurable number of hydrophones. The SUACL's reconfigurability options make it a versatile tool that can meet the diverse experimental needs for underwater communication research. In addition, we also describe the mechanisms of how experimental data are exchanged through the SUACL. Our experimental results demonstrate the feasibility and effectiveness of the proposed SUACL in a shallow-water environment.
      PubDate: TUE, 30 MAY 2023 10:01:24 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Security-Reliability Tradeoffs for Satellite–Terrestrial Relay Networks
           With a Friendly Jammer and Imperfect CSI

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      Authors: Tan Nhat Nguyen;Trinh Van Chien;Dinh-Hieu Tran;Van-Duc Phan;Miroslav Voznak;Symeon Chatzinotas;Zhiguo Ding;H. Vincent Poor;
      Pages: 7004 - 7019
      Abstract: This article proposes and analyzes the reliability and security tradeoff for a satellite–terrestrial (SatTer) relay system. Herein, a satellite sends confidential information to multiple ground users with the help of a relay base station (BS) in the presence of multiple eavesdroppers trying to wiretap the information. In particular, a friendly jammer is deployed near the relay BS to improve secure transmissions. Moreover, the nonidentical Rayleigh fading channels and imperfect channel state information are adopted for a general system model. Then, we consider both amplify-and-forward (AF) and decode-and-forward (DF) relaying strategies to give a full picture of the benefits of each method. In this context, we derive the closed-form expressions of the outage probability and intercept probability corresponding to AF- and DF-based relaying schemes, which is a high challenge and has not been investigated before. Then, Monte-Carlo simulations are conducted to evaluate the correctness of the mathematical analysis and the effectiveness of the proposed methods. Furthermore, the security and reliability trade-off of the SatTer system and the influences of various system parameters (e.g., satellite's transmit power, channel estimation errors, relay's transmit power, fading severity parameter, the average power of light-of-sight, and satellite's multipath components) on the system performance are shown.
      PubDate: MON, 05 JUN 2023 10:02:34 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Multistatic Localization by Differential Time Delays and Time Differences
           of Arrival in the Absence of Transmitter Position

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      Authors: Wenjun Wu;Gang Wang;K. C. Ho;
      Pages: 7020 - 7034
      Abstract: We can only extract the differential time delay (DTD) measurements between the direct and reflected paths in multistatic localization when there is no synchronization in time between the transmitter and receiver and among the receivers. This article first addresses the problem of multistatic localization of a fixed object when the transmitter position is not available by using the DTD measurements. We propose a two-step optimization method for jointly estimating the object and transmitter positions. In the first step, we formulate a nonconvex constrained weighted least squares (CWLS) problem by transforming the DTD measurement model and introducing nuisance variables. Such a nonconvex CWLS problem is then relaxed to a tractable convex semidefinite programming (SDP) problem by applying semidefinite relaxation. In the second step, the error coming from relaxation and approximation in the SDP solution is reduced iteratively through solving a generalized trust region subproblem (GTRS) in each iteration. If the receivers are synchronized such that the time difference of arrival (TDOA) measurements can be acquired in addition to DTD, we formulate a different CWLS problem by utilizing both DTD and TDOA measurements, which is solved by convex relaxation as well. The relaxed SDP problem can achieve the optimal solution of the CWLS problem, and further refinement is no longer needed. We conduct the mean square error (MSE) analysis to validate that both proposed methods are able to achieve the Cramer–Rao Lower Bound (CRLB) performance under small Gaussian noise, which is also validated by simulations.
      PubDate: TUE, 20 JUN 2023 10:02:01 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Approximation-Free Attitude Fault-Tolerant Tracking Control of Rigid
           Spacecraft With Global Stability and Appointed Accuracy

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      Authors: Xiuwei Huang;Zhiyan Dong;Kai Zhang;Feng Zhang;Lihua Zhang;
      Pages: 7035 - 7046
      Abstract: For tumbling or noncooperative targets in space missions, the prior knowledge of the target trajectory is unknown, which leads to the property of prescribed tracking accuracy with assigned settling time hard to achieve. In addition, the unknown inertia characteristics and the presence of actuator faults will also reduce the robustness of the existing spacecraft control methods. To overcome these difficulties, a new approximation-free attitude tracking control scheme of spacecraft with unknown dynamics, external disturbance, and actuator fault is proposed. For the designed controller, no inertia matrix is used, and no approximation-based or adaptive method is applied. Besides, it is rigorously proved that the developed control theory can also ensure that tracking error converges to the prescribed accuracy in appointed time even when the actuator fault occurs. Furthermore, a modified tuning function is used to relax the constraint of the initial value, which ensures the global stability of the closed-loop system. Simulation results verify effectiveness of the proposed method.
      PubDate: THU, 08 JUN 2023 10:01:59 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • TA-LSTM: A Time and Attribute Aware LSTM for Deep Flight Track Clustering

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      Authors: Yuqi Fan;Jiahao Liu;Han Ye;Zengwei Lyu;
      Pages: 7047 - 7060
      Abstract: Flight track clustering is the premise and foundation of air traffic control, and the effective latent representation of track data is key to the flight trajectory clustering. Long short-term memory (LSTM) is an important method to extract the features from trajectory data. However, LSTM ignores the irregular time intervals between track points and fails to effectively distinguish the importance of different attributes at each track point during feature extraction. In addition, the existing methods based on LSTM either ignore the distinct impacts of different attributes at each track point or simply assume that the influences of the previous track points on the subsequent ones decrease with time. In this paper, we propose a flight trajectory feature extraction unit called time and attribute LSTM (TA-LSTM) and a flight track clustering model based on TA-LSTM. The flight track clustering model consists of a feature extraction layer and a clustering layer. The feature extraction layer adopts TA-LSTM to obtain the latent representation of flight tracks, which adds a time control gate and an attribute control gate to the standard LSTM unit. The clustering layer takes the latent representation of flight tracks as the input to obtain the clustering results. We conduct experiments using Automatic Dependent Surveillance-Broadcast track data provided by VariFlight. In comparison with popular and state-of-the-art methods, the proposed method obtains superior performance in three widely used evaluation metrics, i.e., Silhouette Coefficient, Calinski-Harabaz index and Davies–Bouldin index.
      PubDate: TUE, 13 JUN 2023 10:01:08 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Finite-Time Control of a Class of Nonlinear Underactuated Systems With
           Application to Underactuated Axisymmetric Spacecraft

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      Authors: Kang-Kang Zhang;Bin Zhou;Huaiyuan Jiang;Guang-Ren Duan;
      Pages: 7061 - 7071
      Abstract: In this article, the finite-time control problem of a class of nonlinear underactuated systems is addressed. By using the auxiliary state generated by a time-varying oscillator like differential equation and constructing a novel Lyapunov-like function, a time-varying dynamic feedback controller is designed for such nonlinear underactuated systems. It is proved that the state of the closed-loop system converges to zero at the finite time and the controls are bounded. As an application, the finite-time tracking problem for underactuated axisymmetric systems is solved. Numerical simulations verify the effectiveness of the proposed methods.
      PubDate: FRI, 23 JUN 2023 10:01:39 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Koopman-Operator-Based Safe Learning Control for Spacecraft Attitude
           Reorientation With Angular Velocity Constraints

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      Authors: Junyu Yao;Qinglei Hu;Jianying Zheng;
      Pages: 7072 - 7085
      Abstract: This article presents the design of a safe learning attitude controller, based on the Koopman operator (KO), for rest-to-rest spacecraft attitude reorientation under angular velocity constraints. Specifically, a higher-dimensional linear error attitude model is established based on the KO theory and then discretized. An explicit safe learning control strategy with safety-stabilization guarantee is then developed based on the transformed KO model. By performing a loop transformation and convexifying the safety-stabilization conditions, the safe learning controller design is further transformed into a constrained optimization problem, which is independent of the attitude states and thus can be solved offline. Finally, the trained higher-dimensional safe learning controller is mapped to the 3-D attitude controller of the original nonlinear system via the least-squares method for online implementation. In addition, the inner-approximation of the region of attraction (ROA) is provided. Comparison simulations are carried out to validate the effectiveness of the presented strategy.
      PubDate: TUE, 13 JUN 2023 10:01:08 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Spacecraft Depth Completion Based on the Gray Image and the Sparse Depth
           Map

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      Authors: Xiang Liu;Hongyuan Wang;Zhiqiang Yan;Yu Chen;Xinlong Chen;Weichun Chen;
      Pages: 7086 - 7097
      Abstract: Perceiving the 3D structure of the spacecraft is a prerequisite for successfully executing many on-orbit space missions, and it can provide critical input for many downstream vision algorithms. In this paper, we propose to sense the 3D structure of spacecraft using light detection and ranging sensor (LIDAR) and a monocular camera. To this end, Spacecraft Depth Completion Network (SDCNet) is proposed to recover the dense depth map based on gray image and sparse depth map. Specifically, SDCNet decomposes the spacecraft depth completion task into foreground segmentation subtask and foreground depth completion subtask, which segments the spacecraft region first and then performs depth completion on the segmented foreground area. In this way, the background interference to foreground spacecraft depth completion is effectively avoided. Moreover, an attention-based feature fusion module is also proposed to aggregate the complementary information between different inputs, which deduces the correlation between different features along the channel and the spatial dimension sequentially. Besides, four metrics are also proposed to evaluate object-level depth completion performance. Finally, a large-scale satellite depth completion dataset is constructed for training and testing spacecraft depth completion algorithms. Empirical experiments on the dataset demonstrate the effectiveness of the proposed SDCNet, which achieves 0.225 m mean absolute error of interest and 0.778 m mean absolute truncation error, surpassing state-of-the-art methods by a large margin. The pose estimation experiment is also conducted based on the depth completion results, and the experimental results indicate that the predicted dense depth map could meet the needs of downstream vision tasks.
      PubDate: THU, 15 JUN 2023 10:01:31 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Expectation Propagation With Context Adjustment for Smoothing of Jump
           Markov Linear Systems

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      Authors: Elİf Sarıtaş;Umut Orguner;
      Pages: 7098 - 7114
      Abstract: A fixed interval smoother for jump Markov linear systems (JMLSs) is proposed in the framework of expectation propagation (EP). The concept of context adjustment is introduced into EP to avoid/alleviate indefinite covariance problem encountered in standard EP implementations in a systematic way. Kullback–Leibler projection problem for factors involving pseudo-Gaussian likelihoods, which are not proper density functions, is solved and the results are used in the backward pass of the proposed smoother. The simulation results on several scenarios where standard EP has numerical problems show that the proposed smoother has a similar or better performance compared to the alternative methods, which keep the same summary statistics in the literature.
      PubDate: WED, 21 JUN 2023 10:01:42 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Passive TMA in Cluttered Environment From Vertical Arrays and Sonobuoys
           Considering Propagation

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      Authors: Jérémy Payan;Annie-Claude Pérez;Claude Jauffret;Dann Laneuville;
      Pages: 7115 - 7127
      Abstract: This article is devoted to passive submarine target motion analysis (TMA) from data given by sonobuoys and vertical arrays. The set of sonobuoys provides the time difference of arrival measurements and the vertical antennas provide cosines of elevation. The originality of this study comes from the fact that: 1) the measurements are in a cluttered environment; 2) the elevations are those of direct and/or reflected paths; and 3) the sound travel time (also called propagation delay) is taken into consideration. The asymptotic performance is evaluated by the Cramér–Rao lower bound (CRLB) and confirmed by intensive simulations.
      PubDate: THU, 15 JUN 2023 10:01:31 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Characteristic Area-Based Method for Continuous Coverage Analysis of
           Satellites Constellation

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      Authors: Mengyun Zhou;Zhiming Song;Guangming Dai;Maocai Wang;Xiaoyu Chen;
      Pages: 7128 - 7139
      Abstract: Coverage capacity analysis of satellites constellation is the basis of satellite system design and performance evaluation, which are essential in the application of satellite technology. A characteristic area-based method for continuous coverage analysis is proposed in this article. The trajectory characteristics of the Walker constellation are first analyzed. The Earth's surface is partitioned into a group of characteristic areas, and the characteristic distance theory is defined and proposed accordingly. On this basis, the formulations of the characteristic point of each characteristic area and the corresponding characteristic distance with all different situations can be analytically obtained. Additionally, sufficient and necessary conditions for continuous coverage are given accordingly. The complex continuous coverage problem can be converted to the analysis of the relations between the critical geocentric angle of the satellite coverage range and the characteristic distance. Computational results indicate the effectiveness of the proposed method and reveal the feasibility in instructing the constellation design.
      PubDate: MON, 19 JUN 2023 10:07:20 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Planet Craters Detection Based on Unsupervised Domain Adaptation

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      Authors: Zhaoxiang Zhang;Yuelei Xu;Jianing Song;Qing Zhou;Jarhinbek Rasol;Linhua Ma;
      Pages: 7140 - 7152
      Abstract: The detection and localization of craters on the Moon and other planets play an essential role in planet landing, spacecraft navigation, and geologic study. Historically, craters detection involves manually measuring the size and placement of craters in surface images. In this article, we propose an automated pipeline named CraterNet to detect craters on the Moon and Mercury from digital elevation map (DEM) images. Firstly, the convolutional neural network (CNN) based object detection model is trained and tested to detect the craters in a supervised manner. To address the domain discrepancy between the source and target data, an unsupervised domain adaptation (UDA) approach combined with domain randomization is suggested. A causal inference-based feature matching (CIFM) approach integrated with histogram matching is then developed to improve the effectiveness the unsupervised crater detection. The DeepMoon crater dataset and the unsupervised Mercury crater DEMs are introduced in this article to illustrate the applicability and efficacy of the designed method. Results indicate that: 1) The developed approach demonstrates a high performance of supervised crater detection on the DeepMoon dataset, with the F1 and AP scores of 0.786 and 0.804, respectively. 2) The detection model is transferred and performs well on the Mercury dataset in which craters are of different sizes and shapes, as the Precision, Recall, and F1 score for ellipse-shape craters are 0.734, 0.773, and 0.753, respectively. 3) The proposed CraterNet outperforms other deep learning-based segmentation and detection models in terms of crater detection and localization scores.
      PubDate: FRI, 16 JUN 2023 10:01:14 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Constrained Estimation Analysis and Physical Parameters Study for Chain
           Tethered Formation Systems

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      Authors: Guotao Fang;Yizhai Zhang;Yuyao Sun;Fan Zhang;Panfeng Huang;
      Pages: 7153 - 7165
      Abstract: Accurate state estimation of the tethered formation systems (TFS) is challenging due to complex dynamic coupling and elastic tether constraints. In this article, we develop a new fundamental tool to reveal the influence of tether physical parameters on the estimation performance for a class of chain-type TFS. First, we model the elastic tether as a Kelvin–Voigt material to evaluate the influence of tether stiffness and damping on estimation performance. To facilitate analysis, equilibrium configurations are used to characterize the dynamic behavior of chain TFS. Then, we extend the posterior Cramér–Rao lower bound (PCRB) to constrained subspace and derive a recursive form to calculate the constrained PCRB (C-PCRB) for discrete-time filtering with additive Gaussian noises and state equality constraints. Besides, a numerical approach using the particle filter is also developed to approximate the C-PCRB without directly calculating the complex mathematical expectations. Finally, we systematically investigate the influence of the tether stiffness and damping on estimation performance for a chain TFS by extensive simulations.
      PubDate: TUE, 20 JUN 2023 10:02:01 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Polygon-Based Algorithms for N-Satellite Constellations Coverage Computing

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      Authors: Santiago M. Henn;Juan A. Fraire;Holger Hermanns;
      Pages: 7166 - 7182
      Abstract: Satellite coverage analysis is a fundamental performance assessment element in remote sensing and communications' services projects. Coverage is a key parameter in the constellation operation and design for missions relying on several satellites. Since coverage areas over the surface of the Earth change with time, intersecting and drifting apart, the dynamics of every satellite influence the constellation's behavior as a whole. For this reason, every configuration change, be it in the number of satellites or their relative positions, heavily impacts the cost/performance of the mission. This article presents a constellation-to-ground coverage analysis model that enables the rapid evaluation of areas on the surface of the Earth. The method leverages geodetic projections and an oblate-Earth model and uses dynamic transformation and antitransformation techniques combined with polygon Boolean operations. Timestamped datasets are obtained to account for the dynamics of the scenario, which can be exploited in statistical coverage analysis of the constellation. Our empirical evaluations show that this approach is superior in accuracy and computation effort compared with the traditional net-point techniques. While net-point approaches are at the core of the state-of-the-art commercial software, they are approximate. We show that, for finer grid granularity, the net-point schemes converge to our polygon-based results.
      PubDate: MON, 26 JUN 2023 10:03:41 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Dynamic UAV Swarm Confrontation: An Imitation Based on Mobile Adaptive
           Networks

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      Authors: Wei Xia;Zhuoyang Zhou;Wanyue Jiang;Yuhan Zhang;
      Pages: 7183 - 7202
      Abstract: Cooperative unmanned aerial vehicle (UAV) swarms could expand the mission capability of the single UAV and the overall combat effectiveness. We consider the dynamic scenarios where an UAV swarm confronts multiple maneuvering high-value targets (HVTs) and multiple maneuvering attackers. In such hostile scenarios, each UAV of the UAV swarm would generally strive to survive through collaboratively offending their opponents and self-defense from potential attacks. We consider modeling UAV swarms as biologically inspired mobile adaptive networks to imitate the dynamic confrontations of UAV swarms. It is essential to develop an efficient motion control mechanism underpinned by both target tracking and decision making so as to enhance the capability of UAV swarms. We formulate a distributed modularized framework incorporating collaboratively tracking multiple opponents, making successive and prompt decisions, and controlling motion mechanism for mobile adaptive networks. We develop the diffusion multitask interactive Kalman filter to efficiently track multiple preys (HVTs) and multiple predators (attackers). We further develop the collaborative decision making on prey pursuit or predator evasion such that each UAV would acquire early warnings and predictions of the most hypothetically threatening predators to enhance the effectiveness of self-defense. Moreover, we develop a composite motion control mechanism, such that each UAV would be steered toward the prey or away from any approaching predator in a self-organized manner at a variable speed. The modular design of the proposed framework would promote the affordability and adaptability of UAV swarms. Illustrative simulation results validate the efficacy of the proposed distributed framework and the underlying algorithms.
      PubDate: WED, 21 JUN 2023 10:01:42 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • SINS/DVL Linear Initial Alignment Based on Lie Group SE3(3)

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      Authors: Lubin Chang;Hongqiong Tang;Gaoge Hu;Jiangning Xu;
      Pages: 7203 - 7217
      Abstract: This article investigates the strapdown inertial navigation system's (SINS's) initial alignment, aided by the Doppler velocity log (DVL). A linear Kalman filtering method is proposed using the theory of the Lie group, which is immune to initial misalignment. The dead-reckoning position based on the body velocity provided by DVL and attitude provided by SINS is used as measurement. The SINS-calculated attitude, velocity, position, and DVL dead-reckoning position are formulated together as the elements of Lie group $S{E}_3(3)$. Since the dead-reckoning position is a type of left-invariant measurement, the corresponding linear error-state model is derived based on the left-invariant group error definition. Although the Lie group state model is not group affine, the small-attitude-error assumption does not introduce accuracy degeneracy in deriving the linear error-state model. This is the core reason why the derived linear error-state model is still applicable for cases with large initial misalignments. Experiments are conducted to verify the feasibility and effectiveness of the proposed method. The test results show that the proposed method has higher accuracy and faster convergence speed compared with the existing methods.
      PubDate: TUE, 13 JUN 2023 10:01:08 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Fast Trajectory Planning for Approaching Disabled Satellites Based on
           Moving Frame

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      Authors: Peiyun Li;Yunfeng Dong;Xi Ma;
      Pages: 7218 - 7232
      Abstract: Approaching disabled satellites is crucial for missions such as space debris removal and on-orbit servicing. However, the conventional numerical trajectory planning method is time-consuming, making it unsuitable for urgent situations. In this article, an analytical fast trajectory planning method for approaching disabled satellites is proposed. First, an approach moving frame is designed and established to reduce the complexity of the scene. Subsequently, acceleration and angular acceleration are utilized instead of control force and torque while implementing the bang-coast-bang control mode. This allows the differential equations to be solved while maintaining solution optimality. Simulations are presented to validate the effectiveness of the proposed method, demonstrating a significant reduction in planning time cost while maintaining optimality.
      PubDate: THU, 15 JUN 2023 10:01:31 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • On Optimal Power Allocation in Multibeam Multicast NOMA for Satellite
           Communication Systems

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      Authors: Sareh Majidi Ivari;Nazli Ahmad Khan Beigi;Mohammad Reza Soleymani;Yousef Shayan;
      Pages: 7233 - 7251
      Abstract: This article addresses two key challenges in the multibeam multicast nonorthogonal multiple access (MB-MC-NOMA) scheme and respective beamforming design problems in satellite systems. Achieving the max-min fairness and maximum sum-rate among multiple multicast groups of users are jointly considered in a theoretical information framework. It is assumed that each frame contains information of multiple users in multicast transmission. Therefore, contrary to the unicast linear precoding, we have developed the multicast linear precoding with mapping function considering tradeoffs to deal with the lack of the spatial degree of freedom. In our proposed scheme, each beamforming vector conveys information to more than one group of users in an NOMA framework, relying on the superposition techniques at the transmitters and successive interference cancellation (SIC) at the receivers. We have derived the capacity rates achievable in each beam, proposing the methods to maximize the minimum rate and weighted sum-rate. Considering the dependency of the broadcasting power and the respective achievable rates, the equivalent channel and water-filling algorithm for the MB-MC-NOMA is developed; as such, the optimal transmit power density for the groups of users within multiple beams are efficiently computed. The extensive simulation results confirm the proposed theoretical findings, providing a considerable boost in both minimum-rate and sum-rate with respect to state-of-the-art MB-MC satellite systems.
      PubDate: TUE, 20 JUN 2023 10:02:01 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Dynamic Path Planning Algorithm for Unmanned Surface Vehicle Under
           Island–Reef Environment

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      Authors: Jing Zhang;Yani Cui;Guangfu Li;Jia Ren;
      Pages: 7252 - 7268
      Abstract: The marine environment surrounding islands and reefs exhibit significant time-varying characteristics, which require the path planning algorithm of an unmanned surface vehicle (USV) to have good environmental adaptability. To this end, a dynamic path planning algorithm for USV under an island–reef environment is proposed. The algorithm fully considers these environmental features, such as ocean currents, tides, and winds, to construct an environment model. The environment model is combined with the USV motion model to construct a path planning model under the constraint of environmental disturbance time windows. This approach ensures that the path planning model can adapt to the time-varying marine environment. At the same time, to improve the safety of USV navigation by effectively predicting and timely avoiding passing ships, the velocity obstacle method is introduced for establishing a collision risk assessment model that detects the threat level of passing ships in the vicinity of the USV. Based on the decision-making basis generated by the model, a path replanning model is constructed under the constraint of collision detection time windows. This improves the dynamic collision avoidance capability of the path replanning model. Furthermore, to speed up the solution speed and accuracy of the path planning model and the path replanning model, the multiobjective particle swarm optimization algorithm is improved in three aspects: particle coding method, particle update strategy, and external archive maintenance mechanism. Simulation results show that the algorithm can enable USV to safely avoid multiple close-range dynamic obstacles under maritime rules, while also demonstrating a high level of adaptability to the time-varying marine environment.
      PubDate: MON, 26 JUN 2023 10:03:41 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Multiquadrotor Formation Tracking With Mixed Constraints: A Hierarchical
           Rolling Optimization Approach

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      Authors: Si-Sheng Liu;Teng-Fei Ding;Ming-Feng Ge;Xiao-Gang Dong;Zhi-Wei Liu;
      Pages: 7269 - 7280
      Abstract: This article investigates the formation tracking problem of multiquadrotor unmanned aerial vehicles (UAVs) that are subject to mixed constraints including practical constraints on the velocities, attitudes, and input forces. These constraints present significant challenges in regulating and controlling the UAVs. We propose a hierarchical control strategy with a local layer based on model predictive control (MPC) and a cyber layer based on cooperative rolling optimization control (CROC) to simultaneously solve the tracking problems and ensure that corresponding physical states conform to the mixed constraints. In the cyber layer, each node only needs to receive the information from its neighbor nodes to perform synchronous rolling optimization. The cost function of the local open-loop rolling optimization problem is constructed by penalizing the estimated predicted (E-predicted) trajectory and the estimated assumed (E-assumed) trajectory error in the cyber layer. In the local layer, we propose a control algorithm to solve the mixed constraint problem of the cascade control of the quadrotors, which consists of inner-loop attitude control and outer-loop trajectory tracking control. Finally, the simulation results are presented to analyze the performance and the effectiveness of the proposed algorithm.
      PubDate: FRI, 16 JUN 2023 10:01:14 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Distributed Fixed-Time Leader-Following Formation Control for
           Multiquadrotors With Prescribed Performance and Collision Avoidance

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      Authors: Bo Li;Wenquan Gong;Yongsheng Yang;Bing Xiao;
      Pages: 7281 - 7294
      Abstract: This article investigates the distributed fixed-time formation control problem for the multiquadrotor systems with prescribed performance. First, the distributed fixed-time estimators are proposed to accurately estimate the position and velocity information of the leader. Subsequently, the estimator-based position tracking control protocol and attitude controllers are presented, wherein the prescribed performance control technique is utilized. In order to tackle the difficulty of preassigning the settling time of the system, a prescribed-fixed-time formation control protocol is proposed to track the desired trajectories and maintain desired patterns. The stability of the system is guaranteed and collision avoidance among agents is maintained through the implementation of modified hyperbolic cotangent functions and the repulsive potential functions. Finally, numerical simulations are performed to show the efficiency of the proposed scheme.
      PubDate: FRI, 30 JUN 2023 10:02:08 -04
      Issue No: Vol. 59, No. 5 (2023)
       
  • Generalized-Conversion-Based Nonlinear Filtering Using Deterministic
           Sampling for Target Tracking

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      Authors: Jian Lan;
      Pages: 7295 - 7307
      Abstract: For nonlinear filtering, the linear minimum mean square error (LMMSE) estimation is popular. An LMMSE-based estimator using a measurement conversion can outperform the LMMSE estimator using the original measurement. However, to optimally obtain both the dimension and the form of such a conversion is difficult because this involves functional optimization. To solve this problem, this article proposes a generalized-conversion-based filter (GCF) using deterministic sampling (DS). Being an LMMSE-based estimator using a general conversion of the measurement, the estimation performance of the GCF depends only on the conversion-related moments, which are calculated using DS. A constraint on the conversion is used to reduce possible evaluation errors of using a DS method to calculate those moments. The GCF optimizes the moments by obtaining both the optimal dimension and the sample points of the conversion rather than the specific form of it. Then, the final form of the GCF is analytically obtained. For tracking of multiple or maneuvering targets, the likelihood based on the proposed GCF is also derived, and it can be calculated using the obtained conversion sample also in an analytical form. Simulation results demonstrate the effectiveness of the GCF compared with some popular and recently proposed nonlinear estimators, including the LMMSE estimator and existing conversion-based filters.
      PubDate: TUE, 11 JUL 2023 10:01:37 -04
      Issue No: Vol. 59, No. 5 (2023)
       
 
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