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IEEE Transactions on Aerospace and Electronic Systems
Journal Prestige (SJR): 0.611
Citation Impact (citeScore): 3
Number of Followers: 310  
 
  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...

      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Essential Technologies and Concepts for Massive Space Exploration:
           Challenges and Opportunities

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      Authors: Sisay Tadesse Arzo;Dimitrios Sikeridis;Michael Devetsikiotis;Fabrizio Granelli;Rafael Fierro;Mona Esmaeili;Zeinab Akhavan;
      Pages: 3 - 29
      Abstract: The space industry is growing at a tremendous pace generating attraction both from the industry and academia. Various governmental and industrial institutions are embarking on new programs aiming for more exploration of the industry. The impact of recent advances in the control system, computational technology, networking, Internet of Things (IoT), robotics, manufacturing, and machine learning (ML)/artificial intelligence could further support the space industry by providing the possibility of detailed and mass exploration of the deeper space. In that regard, this article reviews this multidiscipline area from the space exploration perspectives. This article focus on the most recent advancement in the aforementioned technologies along with control system theory considering the impact of long-distance between the controlling station and the intended site of exploration. We also provided a case-study analysis for the Martian surface while identifying technical and research challenges.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • A Generalized Passivity-Based Stability Criterion for Assessing Large
           Signal Stability of Interconnected DC Power Systems

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      Authors: Shawn Plesnick;Pritpal Singh;
      Pages: 30 - 38
      Abstract: With the growing need to support large dynamic electric loads in microgrid environments, there has been a necessary push toward highly regulated dc distribution through power electronics. Unfortunately, these systems are nonlinear by nature and provide potentially destabilizing behavior at their source interfaces. To complicate matters further, many systems are built from independently designed subcomponents with little information shared between designs. It is, therefore, necessary to have stability-driven design requirements for each subsystem to enforce stable dynamics upon system integration. Traditional techniques are currently limited to either overly conservative large signal small-gain methods or small signal transfer function approaches that are unreliable for assuring large signal stability. This article proposes a generalized passivity-based stability criterion that conservatively estimates the domain of stability for an integrated nonlinear system. Through passivity partitioning and uniquely driven domain of passivity estimation techniques, this methodology provides interface conditions that certify stability while only requiring a reduced subset of knowledge of each connected subsystem.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Indirect Optimization of Fuel-Optimal Many-Revolution Low-Thrust Transfers
           With Eclipses

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      Authors: Yang Wang;Francesco Topputo;
      Pages: 39 - 51
      Abstract: An efficient indirect method is presented to determine fuel-optimal many-revolution low-thrust transfers in the presence of Earth-shadow eclipses. Specifically, the events of shadow entrance and exit are modeled as interior-point constraints. Following the observation that an ill-conditioned state transition matrix may occur when the spacecraft flies over the edge of the shadow, a two-level continuation scheme is introduced to generate many-revolution trajectories. The established computational framework integrates analytic derivatives, switching detection, and continuation with an augmented flowchart, which yields discontinuous bang-bang solutions and their gradients. Transfers from a geostationary transfer orbit to a geostationary orbit are simulated to illustrate the effectiveness and efficiency of the method developed.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Bistatic Radar Tracking With Significantly Improved Bistatic Range
           Accuracy

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      Authors: Hagay Marom;Yaakov Bar-Shalom;Benny Milgrom;
      Pages: 52 - 62
      Abstract: Tracking with bistatic radar measurements is a challenging problem due to the nonlinear relationship between the radar measurements and the Cartesian coordinates, especially for long distances. This nonlinearity leads, for 3-D bistatic radar, to a nonellipsoidal measurement uncertainty region in Cartesian coordinates, similar to a thin contact lens, that causes consistency problems for a tracking filter. The recently developed conversion of the bistatic radar measurements into Cartesian coordinates enables to maintain consistency by using a converted measurement Kalman filter. However, such a filter suffers from a poor bistatic range accuracy, limiting the multitarget tracking performance in a dense environment of targets or clutter. A solution is suggested by using a filter in the measurement coordinate system and converting its results into Cartesian coordinates. Consistent Cartesian estimation is obtained together with significantly improved filtered bistatic range accuracy. The latter is important in data association, resulting in a measurement-to-track association gates that are over an order of magnitude smaller compared to the Cartesian filters. It is shown that if the data association is performed in sensor coordinates, the (Cartesian) volume of the contact lens association region is over 20 times smaller than if it is performed in Cartesian coordinates because the ellipsoid in the latter case is extremely conservative.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Infrared Small Target Detection via Center-Surround Gray Difference
           Measure With Local Image Block Analysis

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      Authors: Yongsong Li;Zhengzhou Li;Yu Shen;Zhiwei Guo;
      Pages: 63 - 81
      Abstract: Exristing algorithms may suffer from high false alarm rate and low detection probability when detecting dim small target under intricate background clutters and heavy noise. To address this problem, a target detection method based on local image block analysis and center-surround gray difference measure (CGDM) is proposed in this article. First, the infrared image is decomposed into a group of local blocks. The gray information of each block are sorted in ascending order and the last element is deleted, then the maximum gray jump point (MGJP) is extracted by using differential operation. Second, a protection zone is formed with MGJP as the center, and the maximal intensity of protection zone (MIPZ) is extracted. Next, by comparing the gray intensity of MGJP and MIPZ values, the potential target blocks can be reliably selected, while most background blocks and noise blocks will be discarded. After that, the CGDM is presented to enhance the target and suppress background clutters, and then, the center-surround local contrast measure (CLCM) is designed to further suppress the high-intensity clutter residues by searching the target center and revising the protection zone. Finally, the weighted center-surround gray difference measure (WCGDM) is defined by CLCM WCGDM map to recognize real targets. Extensive experiments show that the proposed method outperforms several existing algorithms in small target detection under complex background clutters, and it is robust to various target shapes, target sizes, and noise types.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Energy-Guided Multitarget Detection and Localization for Distributed MIMO
           Radar: Analysis and Solution

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      Authors: Lingxiao Zhu;Gongjian Wen;Yuanyuan Liang;Dengsanlang Luo;Haibo Song;
      Pages: 82 - 97
      Abstract: This article investigates the multitarget detection and localization problem for distributed multiple-input multiple-output radar. We first derive the optimal approach of this issue in the high-dimensional space according to the generalized likelihood ratio test (GLRT) and the maximum likelihood estimation. Moreover, the theoretical multitarget localization accuracy under the considered discrete time signal model is analyzed by invoking the Cram$acute{text{e}}$r–Rao lower bound and the sampling lower bound. To deal with the high complexity problem of the optimal solution, an energy-guided framework including two modules is proposed to detect and locate multiple targets. In the first module, the energy accumulation approach based on the GLRT is employed for multitarget detection, and a fast energy accumulation strategy in terms of the far-field case is developed to improve the computational efficiency. In the second module, an innovative iterative similarity evaluation method is designed to determine the positions of multiple targets by exploiting the relationship between the energy accumulation characteristics and the multitarget existence conditions. The effectiveness of the proposed framework is verified via extensive simulations in both far-field and near-field cases.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • An FPGA-Based General-Purpose Feature Detection Algorithm for Space
           Applications

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      Authors: Yunjie Liu;Xiaofeng Wu;
      Pages: 98 - 108
      Abstract: Spacecraft takes images for applications/missions like attitude determination, astronomy, and space situational awareness. Feature detection is a primary operation in vision-based processing systems. Although different feature detection algorithms are used for specific purposes, they need to achieve invariance by scaling, rotation, and other interference, and output robust results. This article develops a general-purpose feature detection hardware architecture based on the speeded-up robust features (SURF) algorithm. On the other hand, the efficiency of the algorithm can affect the overall system performance to a large extent, especially in real-time operations. This article presents an field-programmable gate array (FPGA)-based implementation of a modified SURF algorithm. The advantages of FPGAs including parallel and pipelining, fixed-point arithmetic, and bitwise operations are fully applied to improve the performance and efficiency of the system in terms of power consumption, and resource utilization.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • A Superresolution Multipath Estimation Algorithm for DSSS Systems

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      Authors: Fengyu Wang;Wei Cui;Jing Tian;
      Pages: 109 - 124
      Abstract: In direct sequence spread spectrum systems, the multipath interference is known to be one of the most important factors in degrading the measurement accuracy. To resolve this problem, we propose a superresolution multipath estimation method based on waveform replacement and adaptive-filtering. In this method, the continuous pseudorandom noise code autocorrelation waveform of the matched filtering output is first replaced by a designed triangular pulse waveform. Subsequently, a reiterative minimum mean-square error adaptive filtering approach is applied to realize superresolution estimation of multipath parameters. The proposed method can achieve accurate multipath estimation even in complex multipath scenarios containing short-delay multipaths and make a good balance between the computational complexity and the estimation performance of multipath parameters. Simulation results verify the effectiveness of the proposed method.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Low-Complexity Iterative Adaptive Approach Based on Range–Doppler
           Matched Filter Outputs

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      Authors: Jing Tian;Biao Zhang;Kun Li;Wei Cui;Siliang Wu;
      Pages: 125 - 139
      Abstract: This article presents a computationally efficient iterative adaptive approach based on range–Doppler matched filter outputs for sidelobe suppression and range–Doppler imaging. A sidelobe suppression scheme, named as dimension reduction based fast iterative adaptive approach (DR-FIAA), is designed by adopting a small processing window on range–Doppler matched filter outputs to eliminate the masking of weak targets by strong targets nearby with low computational complexity. Two specific methods are proposed under this scheme, namely synchronous FIAA (SY-FIAA) and sequential FIAA (SE-FIAA). Compared to SY-FIAA, SE-FIAA has lower computational complexity at the cost of some performance loss. Based on the structure relationships among covariance matrices, further reduced computational complexity can be achieved by SY-FIAA and SE-FIAA. Numerical examples for different scenarios are included to demonstrate the effectiveness of the proposed designs.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • HAPS-Assisted Hybrid RF-FSO Multicast Communications: Error and Outage
           Analysis

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      Authors: Olfa Ben Yahia;Eylem Erdogan;Gunes Karabulut Kurt;
      Pages: 140 - 152
      Abstract: In this article, we study the performance of multiple-hop mixed radio frequency (RF)/free-space optical (FSO) communication-based decode-and-forward protocol for multicast networks. So far, serving a large number of users is considered a promising approach for real-time applications to address the massive data traffic demands. In this regard, we propose two practical use cases. In the former model, we propose a high altitude platform station (HAPS)-aided mixed RF/FSO/RF communication scheme where a terrestrial ground station intends to communicate with a cluster of nodes through two stratospheric HAPS systems. In the latter model, we assume that the line of sight connectivity is inaccessible between the two HAPS systems due to high attenuation caused by large propagation distances. Thereby, we propose a low Earth orbit satellite-aided mixed RF/FSO/FSO/RF communication. For the proposed scenarios, closed-form expressions of outage probability (OP) and bit error rate are derived. In addition, to illustrate the asymptotic behavior of the proposed models, diversity gains are obtained. Furthermore, ergodic capacity and energy efficiency (EE) are provided for both scenarios. Finally, the simulation results are provided to validate the theoretical derivations. The results show that a satellite-aided mixed RF/FSO/FSO/RF scenario achieves better OP, whereas an HAPS-aided mixed RF/FSO/RF scenario can achieve a higher EE.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Motion Estimation and Improved SAR Imaging for Agile Platforms Using
           Omnidirectional Radar and INS Sensor Fusion

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      Authors: Keith T. J. Klein;Faruk Uysal;Miguel Caro Cuenca;Matern P. G. Otten;Jacco J. M. de Wit;
      Pages: 153 - 171
      Abstract: Accurate motion estimation is a challenging problem for agile radar platforms, even when state-of-the-art inertial navigation sensors (INSs) are used. However, it is an important problem to solve as it can have a large impact on the performance of radar modes, such as synthetic aperture radar (SAR). This study addresses the motion estimation problem of agile radar platforms from the perspective of an omnidirectional radar array. In this study, we perform an analysis on the applicability of an omnidirectional radar array to explicitly estimate the motion of an agile SAR platform and improve imaging quality. Building on existing 1-D SAR motion compensation techniques, we develop a method to estimate the 3-D motion of the radar platform utilizing its height and velocity vector. Using a prototype radar developed at the Netherlands Organisation for Applied Scientific Research, we experimentally verify that using the proposed velocity estimation method alone, we achieve comparable positioning performance to that of a state-of-the-art INS, making it possible to perform INS-free SAR imaging by using arbitrary flight paths. We also show that fusing the radar positioning estimates obtained from the proposed methods with the INS output yields an additional increase in SAR imaging performance, improving the resolvability and detectability of weak targets.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • High-Resolution and Wide-Swath SAR Imaging With Sub-Band Frequency Diverse
           Array

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      Authors: Mengdi Zhang;Guisheng Liao;Jingwei Xu;Xiongpeng He;Qi Liu;Lan Lan;Shiyin Li;
      Pages: 172 - 183
      Abstract: High-resolution and wide-swath imaging serves as an important task for synthetic aperture radar (SAR). However, the tradeoff between high azimuth resolution and wide unambiguous swath coverage has not been well optimized in traditional SAR system. Meanwhile, the ultrawideband signal is required to obtain ultrahigh range resolution imaging. It is not easy to increase the bandwidth directly due to the difficulty of system design. To this end, this article makes notable contribution on a sub-band frequency diverse array framework to realize range ambiguous echoes separation and wideband signal synthesis from narrowband signals. By introducing a small frequency increment across the elements within each subarray, the transmit steering vector within each subarray is range-angle-dependent, making it feasible to resolve range ambiguity in the spatial frequency. In addition, the transmitted waveforms of different subarrays occupy different frequency bands in range frequency domain, which play a pivotal role in achieving high-resolution imaging. With phase compensation and sub-band frequency spectrum splicing technique, the wideband signal can be obtained. Simulation results have verified the effectiveness of the proposed approach.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Radar Target Classification Receiver Using Sparse Regression and Target
           Tailored Matched Filters

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      Authors: Caden J. Pici;Sastry Kompella;Ram M. Narayanan;
      Pages: 184 - 195
      Abstract: Waveform design is a commonly used approach to enhance target classification in high resolution radar systems. In the monostatic case of detecting some extended target, inherent to many of these system models is an assumption of a known target impulse or frequency response. A practical issue with this method is that for nonsimple target cases, such a response can change drastically for varying aspect or viewing angles. In this paper, we first develop a sparse regression method for classifying target class and aspect angle. Next, we derive matched filters tailored to dictionaries of target response profiles representing these variations in aspect angle. The desirable goal of real-time classification is met by suitably reducing the computational cost and not relying on a series of measurement and adaption cycles to achieve classification. The result is a series of filters matched to the target dictionary data and can be used in a hypothesis testing approach to classification. Results are shown for radar cross-sectional data generated from computer-aided design models of different targets.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Extended Openmax Approach for the Classification of Radar Images With a
           Rejection Option

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      Authors: Amir Hossein Oveis;Elisa Giusti;Selenia Ghio;Marco Martorella;
      Pages: 196 - 208
      Abstract: The closed-set assumption in conventional classifiers, such as the Softmax, constrains deep networks to select an output from the given known classes. However, the classification in a real-world scenario should account for open sets where a new class of targets, which has not been included in the training phase, can easily confuse the classifier. Therefore, it is necessary to not only correctly classify known classes but also fundamentally deal with unknown ones. In this article, we extend the Openmax approach, which has been introduced for open-set recognition in the optical domain, by offering solutions to its inherent limitations. The motivation behind the work is to propose a more accurate and robust classifier for the open-set recognition problem in synthetic aperture radar (SAR) images, without having any prior knowledge about the incoming unknown data. A number of real-data experiments are conducted to demonstrate the effectiveness of the proposed method on the basis of selected performance metrics. In particular, the Moving and Stationary Target Acquisition and Recognition dataset, which contains SAR images of ten military vehicles, is used for training and inference of a convolutional neural network, with an option to recognize open-set images.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Innovative Solutions Based on the EM-Algorithm for Covariance Structure
           Detection and Classification in Polarimetric SAR Images

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      Authors: Sudan Han;Pia Addabbo;Filippo Biondi;Carmine Clemente;Danilo Orlando;Giuseppe Ricci;
      Pages: 209 - 227
      Abstract: This article addresses the challenge of identifying the polarimetric covariance matrix (PCM) structures associated with a polarimetric synthetic aperture radar (SAR) image. Interestingly, such information can be used, for instance, to improve the scene interpretation or to enhance the performance of (possibly PCM-based) segmentation algorithms as well as other kinds of methods. To this end, a general framework to solve a multiple hypothesis test is introduced with the aim to detect and classify contextual spatial variations in polarimetric SAR images. Specifically, under the null hypothesis, only one unknown structure is assumed for data belonging to a two-dimensional spatial sliding window, whereas under each alternative hypothesis, data are partitioned into subsets sharing different PCM structures. The problem of partition estimation is solved by resorting to hidden random variables representative of covariance structure classes and the expectation–maximization algorithm. The effectiveness of the proposed detection strategies is demonstrated on both simulated and real polarimetric SAR data also in comparison with existing classification algorithms.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Adaptive Nonlinear Incremental Flight Control for Systems With Unknown
           Control Effectiveness

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      Authors: Jing Chang;Roeland De Breuker;Xuerui Wang;
      Pages: 228 - 240
      Abstract: This article exposes that although some sensor-based nonlinear fault-tolerant control frameworks including incremental nonlinear dynamic inversion control can passively resist a wide range of actuator faults and structural damage without requiring an accurate model of the dynamic system, their stability heavily relies on a sufficient condition, which is unfortunately violated if the control direction is unknown. Consequently, it is proved in this article that no matter, which perturbation compensation technique (adaptive, disturbance observer, sliding-mode) is implemented, none of the existing nonlinear incremental control methods can guarantee closed-loop stability. Therefore, this article proposes a Nussbaum function-based adaptive incremental control framework for nonlinear dynamic systems with partially known (control direction is unknown) or even completely unknown control effectiveness. Its effectiveness is proved in the Lyapunov sense and is also verified via numerical simulations of an aircraft attitude tracking problem in the presence of sensing errors, parametric model uncertainties, structural damage, actuator faults, as well as inversed and unknown control effectiveness.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Sequential Spatiotemporal Bias Compensation and Data Fusion for
           Maneuvering Target Tracking

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      Authors: Shizhe Bu;Gongjian Zhou;
      Pages: 241 - 257
      Abstract: In multisensor tracking systems, bias compensation is essential for accurate data fusion. It is challenging to obtain correct fusion results in the presence of both spatial and temporal biases, as well as target maneuver. In this article, a sequential spatiotemporal bias compensation and data fusion method is proposed for maneuvering target tracking with asynchronous multisensor measurements. The spatiotemporal biases of the sensors are augmented into the state vector to be estimated. The state equations are presented to formulate the nearly coordinated turn motion and the nearly constant acceleration motion, respectively, without exactly known measurement interval. In each target motion, the measurements are formulated as functions of both spatiotemporal bias and target state based on the time difference between the measurements and the states to be estimated. Furthermore, the interacting multiple model estimator is incorporated with the unscented Kalman filter to achieve simultaneous sequential estimation of spatiotemporal biases and target states in the presence of target maneuvers. Finally, the posterior Cramer–Rao lower bound for spatiotemporal bias and state estimation is provided. Simulation experiments are performed to demonstrate the effectiveness of the proposed method.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Uncertainty-Aware Variational Inference for Target Tracking

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      Authors: Haoran Cui;Lyudmila Mihaylova;Xiaoxu Wang;Shuaihe Gao;
      Pages: 258 - 273
      Abstract: In the low Earth orbit, target tracking with ground based assets in the context of situational awareness is particularly difficult. Because of the nonlinear state propagation between the moments of measurement arrivals, the inevitably accumulated errors will make the target state prediction and the measurement likelihood inaccurate and uncertain. In this article, optimizable models with learned parameters are constructed to model the state and measurement prediction uncertainties. A closed-loop variational iterative framework is proposed to jointly achieve parameter inference and state estimation, which comprises an uncertainty-aware variational filter (UnAVF). The theoretical expression of the evidence lower bound and the maximization of the variational lower bound are derived without the need for the true states, which reflect the awareness and reduction of uncertainties. The evidence lower bound can also evaluate the estimation performance of other Gaussian density filters, not only the UnAVF. Moreover, two rules, estimation consistency and lower bound consistency, are proposed to conduct the initialization of hyperparameters. Finally, the superior performance of UnAVF is demonstrated over an orbit state estimation problem.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Spatial Filtering for Interference Mitigation in High-Altitude Spaceborne
           GNSS Receivers

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      Authors: Qihui Wei;Yong Gao;Xi Chen;Zhen Huang;Linling Kuang;
      Pages: 274 - 291
      Abstract: Since the antennas of high-altitude spaceborne global navigation satellite system (GNSS) receivers are installed in the satellite nadir direction, interference from Earth poses a potential threat. For interference mitigation in high-altitude spaceborne GNSS receivers, a spatial filtering algorithm is proposed with the constrained minimum spatial mean interference power (CMSMIP) as its metric. Specifically, a spatial mean interference power matrix is conceived based on the spatial probability distribution of the interference sources. The optimization problem for spatial filtering is formulated to minimize the spatial mean interference power expressed by the introduced matrix subject to constraints on the desired signal-to-noise ratio gain. To solve this optimization problem, mathematical properties of the spatial mean interference power matrix are derived, based on which the optimal weight vector is found. The performance of the proposed algorithm is evaluated by simulation with respect to power inversion (PI) as well as minimum variance distortionless response, showing an improved performance at typical interference-to-noise ratios. The proposed algorithm is adaptive to a large number of interference signals and consistent in the performance under different levels of interference power. Furthermore, it can be implemented by retrieving a lookup table of precalculated optimal weight vectors for different GNSS signal directions of arrival, which requires less than 10 MB of memory, instead of requiring frequent snapshots and matrix inversion operations as in conventional spatial filters.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Semi-Supervised Specific Emitter Identification Based on Bispectrum
           Feature Extraction CGAN in Multiple Communication Scenarios

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      Authors: Kaiwen Tan;Wenjun Yan;Limin Zhang;Qing Ling;Congan Xu;
      Pages: 292 - 310
      Abstract: Specific emitter identification (SEI) refers to the technology that uses the inherent defects of the physical layer of a hardware device to identify and uniquely associate a single emitter. Given its unique capability to automatically extract high-dimensional features, deep learning has recently shown great potential in SEI. However, the training process of supervised neural networks largely depends on the generalization of sample distribution and integrity of data labels. In specific noncooperative areas, such as electronic reconnaissance and spectrum monitoring, the unknown radio frequency devices and the complexity of the electromagnetic environment make it difficult to utilize sufficient labeled samples and capture the potential distribution of the data. Therefore, we introduce semi-supervised learning into SEI and propose a self-classification generative adversarial network (GAN) using bispectrum-based feature extraction. The bispectrum estimation of the signal is used as the feature representation of emitters, and the label information is embedded into the input latent layer to guide the training of the GAN. The shared weight update of the classification network is realized through semi-supervised training, and the optimization function of the generator is redefined to solve the mode collapse caused by the game principle. We innovatively extend SEI to communication scenarios with multiple relays and evaluate the effectiveness of our algorithm in different communication scenarios with contaminated radio-frequency fingerprints. The algorithm is also verified using Universal Software Radio Peripheral based on software-defined radio platforms. The numerical experimental results on six modulation signals demonstrate the excellent semi-supervised classification performance of the proposed SEI scheme in multiple scenarios.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Real-Time Crater-Based Monocular 3-D Pose Tracking for Planetary Landing
           and Navigation

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      Authors: Chang Liu;Wulong Guo;Weiduo Hu;Rongliang Chen;Jia Liu;
      Pages: 311 - 335
      Abstract: This article proposes a vision-based framework to track the pose of lander in real time during planetary exploration with craters as landmarks. The contour of landmark crater is represented with three-dimensional Fourier series offline. During tracking, for the first instant, the tracking system is initialized by crater-based correspondence and optimization. For each subsequent instant, with the initial guess from the extended Kalman filter (EKF), the lander pose is determined by L1-norm minimization of the reprojection errors of the crater contour models in the descent image. The covariance of the determined pose is inferred based on Laplace distribution. With this covariance, the EKF generates the final estimate to pose and gives the initial guess for the pose at the next instant. Sufficient trails verify the efficacy of the proposed method.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • GAF Representation of Millimeter Wave Drone RCS and Drone Classification
           Method Based on Deep Fusion Network Using ResNet

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      Authors: Lv Ye;Shengbo Hu;Tingting Yan;Yike Xie;
      Pages: 336 - 346
      Abstract: In recent years, with the rapid increase in the number of drones, the abuse of drones for activities such as terrorist attacks is bound to pose a threat to public safety and privacy. Therefore, it is very important to identify and classify them. Millimeter-wave (MMW) radar provides an effective means to detect drones. And with the development of B5G/6G technologies, a large number of MMW base stations will be deployed in cities in the future. These MMW base stations can be used as MMW radar through simple modification, which greatly saves the detection cost. Radar cross section, as a common data of radar, can provide a data source for drone recognition and classification. Therefore, using the publicly available radar cross section (RCS) data set of drones in the MMW band. We first encode the RCS series into a 2D Gramian angular field (GAF) representation and design a 2D ResNet-10 to classify them. Second, we propose a deep fusion network, which can be used as an RTR with RCS as the information source. The Experimental results show that 2D ResNet-10 is also effective in classifying GAF representations and its time consumption is less than 2D ResNet-18. Compared with other RCS-based classification methods, the performance of deep fusion network is the best.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Predefined-Time Approximation-Free Attitude Constraint Control of Rigid
           Spacecraft

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      Authors: Shuzong Xie;Qiang Chen;Xiongxiong He;
      Pages: 347 - 358
      Abstract: In this article, a predefined-time approximation-free attitude constraint control scheme is proposed for rigid spacecraft with external disturbances. By combining the backstepping technique, an approximation-free controller is systematically developed to maintain the spacecraft attitude within a prescribed small region in predefined time, and the minimum upper bound of the settling time can be exactly given by adjusting a single control parameter. Instead of employing some piecewise continuous functions, the quadratic-fraction functions are constructed in the controllers design to circumvent the possible singularity issue resulted from the differentiation of the virtual controller. With the presented approximation-free control scheme, the computational burden is reduced due to the avoidance of introducing any function approximators. The effectiveness of the proposed scheme is verified by the numerical simulations.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Compatibility Assessment of Multistatic/Polarimetric Clutter Data With the
           SIRP Model

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      Authors: Augusto Aubry;Vincenzo Carotenuto;Antonio De Maio;Francesco Fioranelli;
      Pages: 359 - 374
      Abstract: This article deals with the statistical inference of simultaneously recorded co- and cross-polarized bistatic coherent sea-clutter returns at $S$-band. This study is conducted employing appropriate statistical learning tools, involving the complex envelope of data, to assess the compliance of the available measurements with the spherically invariant random process (SIRP) representation, as well as to analyze possible texture correlations among the diverse polarimetric channels. Moreover, the spatial heterogeneity of the sea-clutter data is studied. The results highlight that the SIRP model is a good candidate for the representation of bistatic coherent clutter and usually the coherence time of the SIRP texture at the bistatic nodes is longer than that in the monostatic sensing. Notably, at bistatic angles in order of $60^circ$, the quadrature components of the cross-polarized bistatic measurements substantially exhibit a Gaussian behavior. These achievements further shed light on the bistatic sea-clutter diversity from the geometric and polarimetric point of view.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Ultra-High-Resolution Spaceborne and Squint SAR Imaging for Height-Variant
           Geometry Using Polynomial Range Model

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      Authors: Byung-Soo Kang;Keewoong Lee;
      Pages: 375 - 393
      Abstract: In this article, we propose an entire processing chain that addresses the geometrical defocusing problems caused by a large orbital arc, a high squint, and topography variations in ultra-high-resolution (UHR) synthetic aperture radar (SAR) operations. Slant-range histories are formulated using fifth-order polynomials to accurately express radar signals with a UHR spaceborne and squint SAR geometry. SAR focusing is first conducted with a constant height assumption. Thereafter, residual topography errors are compensated. Several beneficial contributions are included in the proposed processing chain. First, to achieve constant-height SAR focusing, we improve the typical least-square decomposition-based Stolt interpolations in terms of the image quality and computation time. Second, we clarify how illuminated areas are mapped in a two-dimensional imaging domain after polynomial-based focusing, which is completely different from the well-known relationship between the closest range and zero-Doppler time. Third, a generalized blockwise postfocusing method is devised to correct the range and azimuth defocusing effects induced by height variations. These contributions are verified using numerical simulations. Thus, we can conclude that the proposed method can provide focused SAR images for a height-variant geometry in UHR spaceborne and squint operations.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Reconstruction of Radar Pulse Repetition Pattern via Semantic Coding of
           Intercepted Pulse Trains

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      Authors: Shuo Yuan;Shi-Qian Kang;Wen-Xiu Shang;Zhang-Meng Liu;
      Pages: 394 - 403
      Abstract: The timing structure of multiple successive radar pulses constructs a high-dimensional pattern for intercepted pulse trains, which is called as pulse repetition interval (PRI) pattern. By treating the radar as a machine that uses differently permuted pulses to communicate with surroundings, this pattern acts as the grammatical structure of its language. Compared with the discrete PRI set that is conventionally used for pulse train description, PRI pattern contains richer and more condensed structural information about radar pulse train, which is helpful for pulse deinterleaving and radar recognition. This article introduces the semantic coding theory to reveal and reconstruct PRI pattern from the intercepted radar pulse train. We first define the coding complexity of a pulse train, which is divided into two parts: 1) the complexity of encoding a PRI pattern dictionary with each element consisting of several successive PRIs; and 2) encoding the intercepted pulse train based on this dictionary. The coding complexity is then minimized by optimizing the components in dictionary, and the PRI timing patterns are finally obtained from the dictionary when the minimization is reached. The effectiveness of semantic coding model and PRI pattern reconstruction method is verified in the simulation part.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Efficient LDPC-Coded CCSK Links for Robust High Data Rates GNSS

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      Authors: Rémi Chauvat;Axel Garcia-Pena;Matteo Paonni;
      Pages: 404 - 417
      Abstract: Global navigation satellite system links may require increased data rates to accommodate future features and needs (e.g., precise positioning, authentication, reduction of time-to-first-fix data). A particular form of $M$-ary orthogonal modulation designed for direct-sequence spread-spectrum (DSSS) systems, the cyclic code-shift keying (CCSK) modulation, has been proposed for this purpose. This modulation inherently allows noncoherent processing at receiver side and has the potential to improve the energy efficiency of the data link with respect to classical DSSS/BPSK signals. In this article, $q$-ary ($qin lbrace 2,Mrbrace$) low-density parity-check (LDPC)-based channel coding for $M$-ary CCSK is analyzed, both in terms of robustness and computational complexity. $(q=M)$-ary LDPC-coded CCSK is compared to a bit-interleaved binary LDPC-coded CCSK strategy. Though both solutions provide very reliable links for practical decoding algorithms, it is shown that adequately designed bit-interleaved binary LDPC-coded CCSK signals can offer the additional flexibility inherent to bit-interleaved coded modulation (BICM) while remaining competitive from the point of view of both error rate performance and computational complexity. The latter can be adjusted through the use of incomplete iterative demapping schedules. The optimization of this performance/complexity tradeoff is discussed.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Performance Comparison of Two Subspace-Based GLRTs for Rank-1 Signal
           Detection in Unknown Interference With Multiple Observations

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      Authors: R. S. Raghavan;
      Pages: 418 - 433
      Abstract: This article compares the receiver operating characteristics (ROC) of two subspace-based generalized likelihood ratio tests (GLRTs) referred to in this article as the unconstrained and constrained GLRTs. The tests are derived for slightly different assumptions and are applied to detect an unknown rank-1 signal that belongs to a known $M$-dimensional subspace in $mathbb {C}^{N times 1}$. The receiver is given multiple (say $P$) statistically independent observations of unknown multivariate zero-mean complex Gaussian interference-plus-noise that may contain a rank-1 signal as the test matrix and signal-free training data to estimate the interference-plus-noise covariance matrix. The two detectors are shown to coincide when $M = 1$ and/or $P = 1$. New analytical expressions for the probability of false alarm and probability of detection are derived for the unconstrained GLRT and to the best of our knowledge, no such analytical expressions are available for evaluating the performance of the constrained GLRT in the general case, where $M > 1$ and $P > 1$. The ROC of the unconstrained GLRT is a lower bound on the performance of the constrained GLRT. For small signal-to-interference-plus-noise ratios (SINRs) and $M/N ll 1$, the lower bound is tight, which makes the derived analytical results both interesting and useful.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Two Perpendicularly Colocated Cardioid Sensors of Different Cardioidicity
           Indices/Orders– Their Design Guidelines Based on Their Polar-Azimuthal
           Direction-Finding Cramér–Rao Bound

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      Authors: Hao Yang;Kainam Thomas Wong;Zakayo Ndiku Morris;
      Pages: 434 - 442
      Abstract: A cardioid sensor has a remarkably directional gain pattern of $[ alpha + (1 - alpha) cos (beta) ] cos ^{k}(beta)$, where $alpha$ represents the sensor’s cardioidicity index, $k$ symbolizes the sensor’s directivity order, and $beta$ refers to the incident angle with respect to the cardioid sensor’s axis. Cardioid microphones/hydrophones are commercially available through numerous manufacturers and widely adopted in workaday acoustics. Nonetheless, this letter is first in the open literature to consider any array comprising cardioid sensors of (possibly) different cardioidicity indices/orders. That is, this analysis pioneers the versatile possibility of $(k, alpha)$ being different between the two cardioids that constitute the pair. These two foresaid cardioids are perpendicularly oriented, but spatially colocated in this article. First, their dissimilar orientations facilitate the estimation of an incident signal’s 2-D direction-of-arrival over the azimuth and the elevation, despite the deployment of simply two sensors. Second, their spatial colocation decouples the incident signal’s time/frequency dimensions from the signal’s azimuth/elevation directional dimensions, thus simplifying any subsequent signal-processing computations. This article will analytically explore such a pair’s azimuth/elevation direction-of-arrival estimation precision, as measured by the Cramér–Rao lower bound, producing action-able insights for the system engineer to choose what combination of $(k_{x}, alpha _{x})$ and $(k_{y}, alpha _{y})$ for the two cardioids forming the pair.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Precession Parameter Estimation of Warhead With Fins Based on
           Micro-Doppler Effect and Radar Network

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      Authors: Rongzheng Zhang;Yong Wang;Chunmao Yeh;Xiaofei Lu;
      Pages: 443 - 459
      Abstract: Precession is a typical micromotion of a ballistic missile warhead in the middle of the trajectory. The modulation of the narrow-band radar echo by the precession warhead is mainly reflected in the micro-Doppler effect, which is manifested as the Doppler frequency of the echo periodically changing with time. In the article, an estimation method of precession parameters for warhead with fins using time-frequency analysis and dual radars is proposed. The estimated parameters include precession angle, spinning angular velocity, and coning angular velocity. The geometric and signal model of the precession warhead are established and the analytical expression of the Doppler frequency is deduced. Then, the precession parameters are obtained using the dual view angles of dual radars in this article. Simulation experimental results illustrate the effectiveness of the proposed method.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Radar Cross Section Characterization of Frequency Diverse Array Radar

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      Authors: Huang Bang;Yisheng Yan;Abdul Basit;Wen-Qin Wang;Jie Cheng;
      Pages: 460 - 471
      Abstract: In this article, we develop a frequency diverse array (FDA) radar echo signal model for general complex targets including dumbbell and multiple scatterers. Moreover, we expose the reflectivity characterization, i.e., the radar cross section (RCS) for an FDA radar as a function of fast time, frequency offset, and angle. Note that, previously the RCS is not time dependent for the conventional radars including phased-array radar. Since statistical analysis is not, merely, enough for a fair FDA radar RCS investigations, we further derive the probability distribution function (PDF) of an FDA radar-based RCS by exploiting the statistical distribution characterization with randomly distributed scatterers. Finally, it has been proved that the PDF of an FDA radar RCS is related to both time and frequency offsets. All conclusions are validated by numerical results.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Deep-Learning-Based Model for Accident-Type Prediction During Approach and
           Landing

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      Authors: Yuanyuan Guo;Youchao Sun;Yide He;Fangzhou Du;Siyu Su;Chong Peng;
      Pages: 472 - 482
      Abstract: Modern civil aircraft is designed for a greater workload. While the volume of air traffic has grown exponentially over the past two decades, the total number of accidents has been flat or slightly higher than past averages. However, the fatality and property losses caused by aviation accidents are unbearable even if they happen only once. As reported by a Statistical summary from Boeing in 2019, nearly 40% of the deaths occurred during the final approach and landing. A deep-learning-based model for accident-type prediction during approach and landing was established, which explores the implied association between accident types and accident factors from the risk factors affecting the occurrence of unsafe events. First, based on the investigation reports of civil aircraft fatal accidents in the recent 43 years, 20 common accident risk factors were extracted by the human factors analysis and classification system method. Implicit association of accident factors was excavated by association rules and 6 key factors were extracted from 20 factors. Back propagation neural network, Radial basis function neural network, and Elman neural network applicable to the classification were selected for multiple learning and training. The classification results showed that the comprehensive prediction accuracy of accident types reaches 86.7%, which can effectively determine the types of accidents that may occur during the approach and landing phases of civil aircraft, make emergency measures in advance, and incorporate key accident factors into the assessment scope of civil aircraft safety management system to ensure the aviation operation safety.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Fixed-Time Planetary Landing Guidance With Unknown Disturbance and
           Thruster Constraint

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      Authors: Youmin Gong;Yanning Guo;Dongyu Li;Guangfu Ma;Minwen Guo;
      Pages: 483 - 496
      Abstract: This article proposes a planetary autonomous soft pinpoint adaptive fixed-time feedback landing guidance scheme based on the sliding mode control that is subject to thruster magnitude constraint, unknown control acceleration deviation, and disturbance without a priori knowledge. More specifically, a sliding surface is designed, and its gain is adjusted by the system state, increasing the convergence rate. Then, the adaptive law is proposed to compensate for the thruster magnitude constraint, unknown control acceleration deviation, and disturbance without a priori knowledge. On these bases, a thruster-magnitude-constrained feedback landing guidance is developed to ensure the fixed-time stability of the vehicle even in the presence of unknown control acceleration deviation and disturbance without priori knowledge. Furthermore, numerical simulations are performed to verify the feasibility and effectiveness of the proposed algorithms. Moreover, its near-fuel-optimal performance is illustrated by comparing it with offline fuel-optimal guidance.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Along-Track Boundedness Condition for Spacecraft Relative Motion Around a
           Slowly Rotating Asteroid

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      Authors: Wei Wang;Hanlun Lei;Giovanni Mengali;Alessandro A. Quarta;Hexi Baoyin;
      Pages: 497 - 506
      Abstract: In this paper, the closed-form along-track boundedness condition for spacecraft relative motion around a slowly rotating asteroid is presented. As opposed to the previously proposed criteria where three necessary constraints must be satisfied, the current approach only requires to enforce one constraint to suppress the along-track drift, thus allowing two additional degrees of freedom in spacecraft formation design. In particular, to adapt the derived results applicable for formation with a relatively large size, the boundedness condition via first-order analysis is then extended to obtain a second-order result. In the latter case, the value of mean semimajor axis difference is found to be the root of a quadratic (algebraic) equation. Moreover, an optimal single-impulse maneuver is analyzed, by means of which the bounded relative motion can be guaranteed in the presence of initialization errors. Illustrative examples are provided to validate the analytical results.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Position Awareness Network for Noncooperative Spacecraft Pose Estimation
           Based on Point Cloud

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      Authors: Xiang Liu;Hongyuan Wang;Xinlong Chen;Weichun Chen;Zhengyou Xie;
      Pages: 507 - 518
      Abstract: Spacecraft pose estimation plays a vital role in many on-orbit space missions, such as rendezvous and docking, debris removal, and on-orbit maintenance. At present, the mainstream descriptors-based pose estimation methods ignore the fact that satellite point cloud contains many similar structures, generating numerous mismatched correspondence pairs, and leading to low pose estimation accuracy. This article proposes a position awareness network (PANet) for spacecraft pose estimation. Specifically, the point cloud is first fed into a hierarchical embedding network to extract the key points and construct local structural descriptors. We also build the relative position features by encoding the relative position between key points and reference points. The matching matrix between point clouds is then calculated by comprehensively considering the local structure descriptors and relative location features. In this way, the problem of ambiguous matching caused by similar local structures is avoided. Finally, weighted singular value decomposition (SVD) is utilized to solve the pose between the point clouds based on the correspondence pairs generated by the matching matrix. Besides, a large-scale satellite point cloud dataset is also constructed for training and testing pose estimation algorithms. Empirical experiments on the dataset demonstrate the effectiveness of the proposed PANet, which achieves 1.18$^circ$ rotation error and 0.136 m translate error, surpassing state-of-the-art methods by a large margin.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Adaptive Multiple-Model-Based Fault-Tolerant Control for Non-minimum Phase
           Hypersonic Vehicles With Input Saturations and Error Constraints

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      Authors: Le Wang;Ruiyun Qi;Liyan Wen;Bin Jiang;
      Pages: 519 - 540
      Abstract: In this article, an adaptive multiple-model-based fault-tolerant controller is developed for nonminimum phase air-breathing hypersonic vehicles (HSVs) in the presence of parametric uncertainties, elevator faults, input saturations, and time-varying error constraints. Compared with the existing works, the elevator-to-lift couplings are taken into account, which makes the HSV longitudinal models exhibit unstable internal-dynamics that impedes the applicability of common nonlinear control methods for control design. In order to solve this problem, a two-layer cascade control strategy is proposed in which the external inputs control the external states and the external state deviations control the internal states: 1) A multiple-model linear quadratic control strategy, based on gap metric, is proposed to guarantee the stability of the internal dynamics; 2) a fault-tolerant control scheme, based on tan-barrier Lyapunov functions, is developed by using a low-pass filter and an auxiliary system in conjunction with adaptive backstepping design. In the control law, the uncertain parameters are replaced by their estimates updated by adaptive laws. Additionally, the stability of the whole system is rigidly proved through standard Lyapunov approach, while the other states and signals in the closed-loop system are guaranteed to be bounded. Simulation results are provided to illustrate the effectiveness of the proposed adaptive multiple-model-based fault-tolerant controller.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Image Segmentation for Radar Signal Deinterleaving Using Deep Learning

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      Authors: Mustafa Atahan Nuhoglu;Yasar Kemal Alp;Mehmet Ege Can Ulusoy;Hakan Ali Cirpan;
      Pages: 541 - 554
      Abstract: Passive systems, such as electronic intelligence and electronic support measures systems, aim to extract necessary information from the received radar signals for situational awareness. To achieve this, the system must first deinterleave the radar signals simultaneously coming from different emitters, so that the pulse repetition interval (PRI) patterns will be revealed for further analysis and identification purposes. PRI transform is a well-known deinterleaving method that utilizes the complex autocorrelation function. There are two main versions of the method. The initial version detects only constant PRI schemes, while the second modified version is capable of detecting varying PRI schemes as well. Miss detection of varying PRI patterns is the drawback for the first version, while producing harmonics, especially at high PRI levels, is the disadvantage of the second one. To alleviate these problems, we propose an image segmentation method based on deep learning. The developed preprocessing step uses both versions of the PRI transform outputs to generate 2-D time–PRI images of the collected radar emissions, so that constant and varying PRI patterns are revealed. The images are concatenated and fed to the proposed network, which uses a practicable U-Net structure. The output of the network directly estimates the PRI levels of the existing radars and the time duration of the transmission jointly. In addition to qualitative and quantitative experiments on the synthetic datasets, qualitative experiments are conducted on real measurements, in which we demonstrate that the proposed method effectively utilizes PRI transform in the preprocessing step and outperforms both versions of the PRI transform in terms of accuracy, Jaccard index, structural similarity, and PRI estimation error metrics.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • One-Bit Digital Beamforming

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      Authors: Xinzhu Chen;Lei Huang;Hanfei Zhou;Qiang Li;Kai-Bor Yu;Wenxian Yu;
      Pages: 555 - 567
      Abstract: One-bit quantization technique is able to lower power consumption, save storage space, and reduce system cost, which, when tailored for radar sensing, is applicable to small-size platforms, such as unmanned aerial vehicle, small satellite and missile. Prosperous research works on one-bit signal processing have been performed in radar applications. Among them, beamforming with one-bit measurements has not been thoroughly discussed yet. This article addresses the issue of one-bit digital beamforming on receive. First, we revisit the theorem on one-bit quantization generating harmonics. This article extends to analyze the quantization impact on array signals, that is, the dispersive effect, by establishing array signal model and constructing corresponding correlation matrix. The dispersion incurs spatial filter mismatch when forming beams, which poses a big challenge for narrowband digital array systems. This article proceeds to propose a strategy of subband processing for digital receiving and one-bit beamforming. The fundamental and harmonic beams are formed separately within subbands and characterized, respectively. Eventually, two advanced applications are presented for utilizing the fine angular-resolution harmonic beams instead of suppression. One is wide area search for high-speed targets and the other is jamming cancellation. Fruitful simulation results are provided to confirm our theoretical findings.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Learning-Based Distributed Containment Control for HFV Swarms Under
           Event-Triggered Communication

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      Authors: Renwei Zuo;Yinghui Li;Maolong Lv;Zehong Dong;
      Pages: 568 - 579
      Abstract: This article proposes a learning-based distributed containment control protocol for a team of hypersonic flight vehicles (HFVs) composed by some leaders and some followers in the presence of switching event-triggered communication. In contrast with most existing results concerning HFVs, the crucial characteristics of our design lie in that all leader and follower HFVs are empowered with a distributed cooperative learning capability in the sense that neural networks weights are not required to be adapted all the time, in that all follower HFVs are retained in the convex hulls spanned by leader HFVs all the time, and in that the communication among distinct HFVs is conducted based on an event-triggered strategy that utilizes a switching threshold. More precisely, by utilizing the learned knowledge represented by constant neural networks, an experience-based distributed control protocol is further proposed without the need for online adaptation. Numerical simulations studies on a group of HFVs have been conducted to verify the effectiveness of the presented results.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Globally Optimized TDOA High-Frequency Source Localization Based on
           

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      Authors: Wenxin Xiong;Christian Schindelhauer;Hing Cheung So;
      Pages: 580 - 590
      Abstract: We investigate the problem of high-frequency (HF) source localization using the time-difference-of-arrival (TDOA) observations of ionosphere-refracted radio rays based on quasi-parabolic (QP) modeling. An unresolved but pertinent issue in such a field is that the existing gradient-type scheme can easily get trapped in local optima for practical use. This will lead to the difficulty in initializing the algorithm and finally degraded positioning performance if the starting point is inappropriately selected. In this article, we develop a collaborative gradient projection (GP) algorithm in order to globally solve the highly nonconvex QP-based TDOA HF localization problem. The metaheuristic of particle swarm optimization (PSO) is exploited for information sharing among multiple GP models, each of which is guaranteed to work out a critical point solution to the simplified maximum likelihood formulation. Random mutations are incorporated to avoid the early convergence of PSO. Rather than treating the geolocation of HF transmitter as a pure optimization problem, we further provide workarounds for addressing the possible impairments and challenges when the proposed technique is applied in practice. Numerical results demonstrate the effectiveness of our PSO-assisted reinitialization strategy in achieving the global optimality, and the superiority of our method over its competitor in terms of positioning accuracy.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Receding Horizon-Based Infotaxis With Random Sampling for Source Search
           and Estimation in Complex Environments

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      Authors: Minkyu Park;Pawel Ladosz;Jongyun Kim;Hyondong Oh;
      Pages: 591 - 609
      Abstract: This article proposes a receding horizon-based information-theoretic source search and estimation strategy for a mobile sensor in an urban environment in which an invisible harmful substance is released into the atmosphere. The mobile sensor estimates the source term including its location and release rate by using sensor observations based on Bayesian inference. The sampling-based sequential Monte Carlo method, particle filter, is employed to estimate the source term state in a highly nonlinear and stochastic system. Infotaxis, the information-theoretic gradient-free search strategy is modified to find the optimal search path that maximizes the reduction of the entropy of the source term distribution. In particular, receding horizon Infotaxis (RHI) is introduced to avoid falling into the local optima and to find more successful information gathering paths in obstacle-rich urban environments. Besides, a random sampling method is introduced to reduce the computational load of the RHI for real-time computation. The random sampling method samples the predicted future measurements based on current estimation of the source term and computes the optimal search path using sampled measurements rather than considering all possible future measurements. To demonstrate the benefit of the proposed approach, comprehensive numerical simulations are performed for various conditions. The proposed algorithm increases the success rate by about 30% and reduces the mean search time by about 40% compared with the existing information-theoretic search strategy.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Accounting for Acceleration—Signal Parameters Estimation Performance
           Limits in High Dynamics Applications

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      Authors: Hamish McPhee;Lorenzo Ortega;Jordi Vilà-Valls;Eric Chaumette;
      Pages: 610 - 622
      Abstract: The derivation of estimation lower bounds is paramount to designing and assessing the performance of new estimators. A lot of effort has been devoted to the range-velocity estimation problem, a fundamental stage on several applications, but very few works deal with acceleration, being a key aspect in high dynamics applications. Considering a generic band-limited signal formulation, we derive a new general compact form Cramér–Rao lower bound (CRB) expression for joint time-delay, Doppler stretch, and acceleration estimation. This generalizes and expands upon known delay/Doppler estimation CRB results for both wideband and narrowband signals. This new formulation, especially easy to use, is created based on baseband signal samples, making it valid for a variety of remote sensors. The new CRB expressions are illustrated and validated with representative GPS L1 C/A and linear frequency modulated chirp band-limited signals. The mean-square error of a misspecified estimator (conventional delay/Doppler) is compared with the derived bound. The comparison indicates that for some acceleration ranges the misspecified estimator outperforms a well-specified estimator that accounts for acceleration.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • BER Reduction Using Partial-Elements Selection in IRS-UAV Communications
           With Imperfect Phase Compensation

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      Authors: Sobia Jangsher;Mohammad Al-Jarrah;Arafat Al-Dweik;Emad Alsusa;Mohamed-Slim Alouini;
      Pages: 623 - 633
      Abstract: This article considers minimizing the communications bit error rate (BER) of unmanned aerial vehicles when assisted by intelligent reflecting surfaces. By noting that increasing the number of IRS elements in the presence of phase errors does not necessarily improve the system’s BER, it is crucial to use only the elements that contribute to reducing such a parameter. To this end, we propose an efficient algorithm to select the elements that can improve BER. The proposed algorithm has lower complexity and comparable BER to the optimum selection process, which is an NP-hard problem. The accuracy of the estimated phase is evaluated by deriving the probability distribution function (PDF) of the least-square channel estimator, and showing that the PDF can be closely approximated by the von Mises distribution at high signal-to-noise ratios. The obtained analytical and simulation results show that using all the available reflectors can significantly deteriorate the BER, and thus, partial element selection is necessary. It is shown that, in some scenarios, using about 26% of the reflectors provides more than tenfold BER reduction. The number of selected reflectors may drop to only 10% of the total elements. As such, the unassigned 90% of the elements can be allocated to serve other users, and the overhead associated with phase information is significantly reduced.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Multi-Timeslot Wide-Gap Frequency-Hopping RFPA Signal and Its Sidelobe
           Suppression

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      Authors: Xingwang Long;Wenhao Wu;Kun Li;Ju Wang;Siliang Wu;
      Pages: 634 - 649
      Abstract: Random frequency and pulse repetition interval agile (RFPA) signals have excellent anti-jamming ability and achieve low probability of intercept (LPI), making them promising for applications in radar systems. However, their matched filter (MF) outputs suffer from random range-velocity sidelobes. In the range dimension, these sidelobes can be classified into two categories: the distant sidelobe floor spread out beyond the minimum interval between pulses, and the near sidelobe plateau confined within about one pulsewidth. These sidelobes seriously degrade the target detection capability of RFPA signals. To address this issue, we propose the concept of a multi-timeslot wide-gap frequency-hopping sequence (multi-timeslot WGFHS) and use it in the design of RFPA signals. In doing so, the distant sidelobes are easily suppressed with a simple low-pass filter (LPF) in the receiver if the parameters of the multi-timeslot WGFHS are chosen properly, while the remaining near sidelobes are suppressed by the iterative adaptive approach based on matched filter outputs (MF-IAA). Simulation results show that the proposed method can effectively suppress the sidelobes of RFPA signals, accurately recover the range-velocity images, and successfully detect weak targets in the presence of strong targets or clutter.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Suzuki Distributed Monostatic and Bistatic S-Band Radar Sea Clutter

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      Authors: Stephen Bocquet;
      Pages: 650 - 659
      Abstract: Low grazing angle S-band radar sea clutter data, collected with the NetRAD multistatic radar system, are analyzed. The Suzuki distribution, a compound Gaussian model with log normal texture, is found to be a good model for both monostatic and bistatic clutter. Clutter textures from simultaneously recorded monostatic and bistatic data are interpreted using hydrodynamics, providing estimates of the water depth and wave direction.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Tri-Cardioid Co-Centered Co-Planar Array—Its Direction-Finding
           Cramér-Rao Bound and Design Guidelines

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      Authors: Junlong Chen;Kainam Thomas Wong;Zakayo Ndiku Morris;
      Pages: 660 - 677
      Abstract: Cardioid microphones/hydrophones have a highly directional gain pattern of $[ alpha + (1 - alpha) cos (beta) ] cos ^{k}(beta)$, where $k$ refers to the sensor's directivity order, $alpha$ denotes the same sensor's cardioidicity index, and $beta$ represents an impinging signal's incident direction of arrival relative to the cardioid sensor's axis. Three such cardioids organized in orthogonal orientation in three Cartesian spatial dimensions and in spatial colocation as one sensing unit—such a 3-D triad has already attracted much recent attention in the research literature. However, not all three Cartesian coordinates have equal importance to many acoustical applications, which focus alternatively on the azimuthal direction defined on a flat plane but less on the elevation direction normal to that plane. So, this article will instead analyze a 2-D planar configuration of three colocated/cocentered cardioids differently oriented azimuthally apart by $120^circ$. This aforesaid coplanar triplet conforms to a flat supporting surface more than a Cartesian tridimensionally perpendicular triad can. For such a coplanar triplet composed of cardioids preset at any specific $(k, alpha)$, the triplet's polar/azimuthal direction-finding Cramér–Rao lower bounds will be analytically derived in closed forms here in this article. Those bounds will be uncovered to exhibit intricate mathematical structures, which will be dissected in detail to yield refined insights, producing simple “actionable” rules-of-thumb to guide the system engineer to choose the appropriate values for $(k, alpha)$.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Probabilistic Analysis of the Amplitude-Phase Error Tolerance for
           Cross-Eye Jamming

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      Authors: Yongcai Liu;Liang Zhou;Fangmin He;Dong Li;Jin Meng;
      Pages: 678 - 684
      Abstract: Cross-eye jamming is a technique to counter monopulse radar. The impact of the amplitude-phase error on the jamming performance is one of the key problems for cross-eye jamming. The tolerance of amplitude-phase error for cross-eye jamming has been analyzed by deterministic analysis. However, the amplitude-phase errors of a practical jammer are commonly stochastic variables due to the variation of working conditions. In this article, the probabilistic analysis of the amplitude-phase error tolerance for cross-eye jamming is studied. By demonstrating that the contours of cross-eye gain can be approximate to a set of ellipses, the probability distributions of cross-eye gain and angle factor are derived, based on the assumption that the amplitude-phase errors of cross-eye jammer are Gaussian distributed. The article presents the instructions to determine the required standard deviation of the amplitude-phase error to produce a specific confidence level of cross-eye jamming performance metric.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Impact Analysis of Intercell Interference in Cellular Networks for
           Navigation Applications

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      Authors: Pai Wang;Y. Jade Morton;
      Pages: 685 - 694
      Abstract: This article studies the intercell interference (ICI) effects in cellular networks for navigation applications. This is achieved through the derivation of an analytical expression for a cellular navigation receiver postcorrelation signal-to-noise power ratio (SNR) in the presence of multiple asynchronous cells. It reveals that in addition to the channel power, the cell loading rate and data modulation order for the interfering cells also play important roles in affecting the received signal quality of the desired cell. Furthermore, the time-of-arrival (TOA) estimation and positioning accuracy degradation due to the ICI is characterized by the Cramer–Rao and Ziv–Zakai lower bounds based on the derived postcorrelation SNR. Simulations are performed to verify the theoretical expressions and the results indicate that the ICI term can be treated as an additional Gaussian disturbance for characterizing the TOA estimation accuracy in cellular navigation receivers.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • On GLRT-Based Detection of One-Sided Composite Signal in Unimodal Noise

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      Authors: Berkan Dulek;
      Pages: 695 - 700
      Abstract: Convexity properties of detection probability are investigated in generalized likelihood ratio test-based detection of one-sided composite signal corrupted by additive unimodal noise. It is shown that the detection probability of the corresponding threshold detector is a concave function of normalized signal power at low and high signal-to-noise ratio (SNR). For intermediate values of SNR, the behavior can be explicitly characterized based on the derivative of the logarithm of the channel noise density and the detector threshold. Optimal time-sharing strategies of average power-limited transmitters and jammers are established. Numerical examples are provided for Gaussian, Laplace, and Cauchy densities.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Analysis of Error Rate and Capacity in the Presence of I/Q Imbalances Over
           an Impulsive Noise Channel

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      Authors: Geunbae Kim;Dongweon Yoon;
      Pages: 701 - 711
      Abstract: Many state-of-the-art unmanned aerial vehicle-assisted communication systems adopt quadrature amplitude modulation (QAM) and zero intermediate frequency (IF) architecture. In zero-IF QAM devices, in-phase/quadrature (I/Q) amplitude and phase imbalances cause severe performance degradation. In addition, the performance of the devices in close proximity to the impulsive noise sources can also be significantly degraded. In this article, we derive and analyze the symbol error rate (SER) of M-ary square QAM and the channel capacity of a system in the presence of I/Q imbalance over an impulsive noise channel. We first present an exact expression for the joint probability density function (PDF) of the demodulated I/Q signals affected by impulsive noise. By using the derived PDF, we then provide exact expressions for SER and SER floor of M-ary square QAM and average and asymptotic channel capacities of the system with I/Q amplitude and phase imbalances over an impulsive noise channel. We validate the theoretical results through computer simulations.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Event-Triggered Consensus LMB Filter for Distributed Multitarget Tracking

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      Authors: Gaiyou Li;Giorgio Battistelli;Luigi Chisci;Lin Gao;Ping Wei;
      Pages: 712 - 719
      Abstract: This correspondence investigates the problem of reducing energy and bandwidth consumption of a sensor network for distributed multitarget tracking by the labeled multi-Bernoulli filter. To this end, an event-triggered method is adopted together with a consensus strategy for each sensor node to transmit only Bernoulli components (BCs) that achieve enough information gain. Two message transmission strategies, i.e., joint and independent transmission, are devised. The possibility to separately trigger BCs allows the proposed method to better tradeoff tracking performance versus communication load with respect to existing distributed multitarget tracking approaches.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Cross-Band Correlator and Detector Design for Robust GNSS Multifrequency
           Combined Acquisition

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      Authors: Jihong Huang;Rong Yang;Xingqun Zhan;
      Pages: 720 - 733
      Abstract: This article proposes a cross-band (CB) correlator and detector for the multifrequency (MF) global navigation satellite system (GNSS) receiver to facilitate the development of robust acquisition in challenging environments. The CB correlator is designed with the projection and weighting operations to combine the multiple signals on different bands in an artificial reference domain. Based on the generalized likelihood ratio test (GLRT), the corresponding decision variable is derived to construct the CB detector considering the combination coefficients and detection threshold. The effectiveness of the proposed design is verified by Monte Carlo simulation and field test. The theoretical results show the improved performance of CB combinations when the signals suffer attenuations or frequency selective fading. The realistic results show the enhanced robustness of GPS and BeiDou Navigation Satellite System (BDS) CB detections in the presence of possible signal blockages and multipath interferences in the urban canyon.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • Nonlinear Spline Versoria Prioritization Optimization Adaptive Filter for
           Alpha-Stable Clutter

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      Authors: Wenyan Guo;Yongfeng Zhi;
      Pages: 734 - 744
      Abstract: The increasing demand for a better quality of service in radar and wireless communications has attracted research on interference suppression. Nonlinear filtering methods based on spline architecture have been widely utilized in signal processing due to their effectiveness against alpha-stable interference. However, existing nonlinear spline adaptive filter algorithms suffer from high steady-state misalignment. To achieve lower steady-state misalignment along with having comparable computational complexity, we propose a nonlinear spline Versoria prioritization optimization adaptive filter (SPOAF-MVC) for alpha-stable clutter in this article. Furthermore, we study the bound on learning rate and computational complexity for the proposed algorithm. Numerical simulations confirm the effectiveness and efficiency of the proposed SPOAF-MVC algorithm for alpha-stable clutter under the Wiener system identification.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
  • An Analytical Study on Functional Split in Martian 3-D Networks

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      Authors: Stefano Bonafini;Claudio Sacchi;Riccardo Bassoli;Fabrizio Granelli;Koteswararao Kondepu;Frank H.P. Fitzek;
      Pages: 745 - 753
      Abstract: As space agencies are planning manned missions to reach Mars, researchers need to pave the way for supporting astronauts during their sojourn. This will also be achieved by providing broadband and low-latency connectivity through wireless network infrastructures. In such a framework, we propose a Martian deployment of a 3-D network acting as cloud radio access network (C-RAN). The scenario consists of unmanned aerial vehicles (UAVs) and small satellite platforms. Thanks to the thin Martian atmosphere, CubeSats can stably orbit at very-low-altitude. This allows to meet strict delay requirements to split baseband processing functions between drones and CubeSats. The detailed analytical study, presented in this article, confirmed the viability of the proposed 3-D architecture, under some constraints and tradeoff concerning the involved network infrastructures, that will be discussed in detail.
      PubDate: Feb. 2023
      Issue No: Vol. 59, No. 1 (2023)
       
 
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