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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: 309  
 
  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

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      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Special Issue on Industrial Information Integration in Space Applications

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      Authors: Andrew W. H. Ip;Zhuming Bi;Madjid Tavana;Brij B Gupta;Khanh Dai Pham;
      Pages: 4860 - 4863
      Abstract: The papers in this special section focus on industrial information integration in space applications. With continuous growth in the complexity, scale, and dynamics of space systems, information integration (II) becomes an essential strategy for managing system complexity and tackling dynamic changes and uncertainties in space missions. Space II (SII) is in high demand so as to meet the system requirements of latency, heterogeneity, communication, networking, security, and resilience. The study of SII has attracted much attention from scientists and engineers across all engineering domains. The papers in this section identifies new theories, methodologies, tools, and case studies of SII that are developed to address some unique challenges of space systems such as security, safety, reliability, and resilience and help T-AES readers gain a basic understanding of the cutting-edge space technologies and the directions of future advancement of SII.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Hierarchical Domain-Based Multicontroller Deployment Strategy in
           SDN-Enabled Space–Air–Ground Integrated Network

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      Authors: Chen Chen;Zhan Liao;Ying Ju;Ci He;Keping Yu;Shaohua Wan;
      Pages: 4864 - 4879
      Abstract: The space–air–ground integrated network (SAGIN) is considered to be a significant framework for realizing the vision of “6G intelligent connection of all things.” A typical SAGIN consists of three parts: a space-based network composed of various orbiting satellites, an air-based network composed of aircraft, and a traditional ground-based network. Considering the cost of satellite launch, the network needs to be flexible and controllable. In order to ensure that the ground can handle satellite anomalies in real time by program, it is necessary to introduce in-orbit programmable networks, such as the software-defined network (SDN). In the network management architecture, if the controller plane in the SDN adopts the flat management scheme, the expansion of the control plane is limited due to the low efficiency of data synchronization among controllers. Compared with controller deployments on terrestrial networks, multicontroller deployments in the SAGIN face the following problems: the dynamic change of the satellite network topology, the large-scale network nodes, the increase or decrease in the number of aerial vehicles, and the unbalanced distribution of ground users. Therefore, it is of great significance to study how to optimize the deployment of multiple controllers in the SDN-enabled SAGIN. This article introduces an SDN into the SAGIN and designs a hierarchical domain-based SDN-enabled SAGIN architecture. A multicontroller deployment strategy for the hierarchical domain-based SDN-enabled SAGIN is proposed. First, we divide the SDN control plane into two layers, i.e., the primary controller layer is deployed on the ground network and the secondary on the space-based network. The SDN data plane is composed of space-based, air-based, and ground-based networks. Second, considering the average network delay and the controller load, a multiobjective optimization model is constructed. To de-ermine the number of controllers and the relative positions of switch nodes and controllers, the clustering algorithm based on k-means is adopted to initially divide the data plane. Finally, to improve the global search ability of the algorithm, a multiobjective optimization algorithm based on a genetic algorithm is adopted. The simulation results show that the proposed strategy is effective in reducing the average network delay and improving the controller load balance. Compared to other algorithms, the average network delay is reduced by 13.3% and the controller load is improved by 10.33%.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • A Patch-Based Composite Denoising Algorithm for Wireless Transmission

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      Authors: Shihong Yao;Zhigao Zheng;Tao Wang;
      Pages: 4880 - 4890
      Abstract: Many image denoising approaches in recent years are based on patch processing. Particularly, patch-based denoising algorithms under the low-rank (LR) model show outstanding performance in wireless transmission. However, constructing proper dictionaries is a key issue that affects the denoising results. On the basis of this idea, we propose a patch-based composite method for image denoising named similar patch LR (SPLR), which introduces a nonlocal model and structure similarity index measure (SSIM) to evaluate patches similarity. This evaluation method can explore the similarity of image patches to the full extent and construct a similar patches dictionary with an LR property. In addition, to improve the robustness of denoising, we divide the noise part into two parts, and use $L_{0}$$-$ and $L_{2,0}$$-$ norm, two different norm constraints to manage different types of noise. Extensive experiments show that the denoising performance and the robustness to noise of SPLR is better than that of the widely used and state-of-the-art, LR image denoising algorithms.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Application of Augmented Spread Spectrum Time Domain Reflectometry for
           Detection and Localization of Soft Faults on a Coaxial Cable

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      Authors: Xudong Shi;Ruipu Li;Haotian Zhang;Hongxu Zhao;Yang Liu;
      Pages: 4891 - 4901
      Abstract: Aviation cables are an essential part of aircraft, which transmits signals and power. Aviation cable faults often occur due to drastic changes in temperature and humidity and the effects of vibration. Obvious cable faults, such as open circuits and short circuits, can be detected during maintenance. However, some soft faults like the loose connection of the connector, damage of the insulation layer, and improper cable bending radius cannot be detected. Spread spectrum time domain reflectometry (SSTDR) is a diagnostic method for cable faults; however, the amplitude of the reflected signal is low when the soft fault occurs. SSTDR is only sensitive to apparent faults, but it is insufficient to detect soft faults. This article proposes an aviation cable fault detection and location method, augmented spread spectrum time domain reflectometry (ASSTDR), to detect soft faults effectively. First, the SSTDR results are decomposed into some intrinsic mode functions (IMFs) using the improved variational mode decomposition and selects the IMF with the highest kurtosis to analyze. Second, short-time Fourier transform (STFT) is applied to deal with the selected IMF to enhance the amplitude characteristics. Then, reassign spectrogram is computed to improve the resolution of STFT time-frequency distribution images to locate the location of soft faults. Finally, the effectiveness of ASSTDR is verified by experiments with different fault types and fault degrees. The comparative experiments demonstrate that the method can accurately detect soft faults.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Space Noncooperative Object Active Tracking With Deep Reinforcement
           Learning

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      Authors: Dong Zhou;Guanghui Sun;Wenxiao Lei;Ligang Wu;
      Pages: 4902 - 4916
      Abstract: Actively tracking an arbitrary space noncooperative object relied on visual sensor remains a challenging problem. In this article, we provide an open-source benchmark for space noncooperative object visual tracking including simulated environment, evaluation toolkit, and a position-based visual servoing (PBVS) baseline algorithm, which can facilitate the research in this topic especially for those methods based on deep reinforcement learning. We also present an end-to-end active visual tracker based on deep Q-learning, named as DRLAVT, which learns approximately optimal policy merely took color or RGBD images as input. To the best of authors knowledge, it is the first intelligent agent used for active visual tracking in aerospace domain. The experiment results show that our DRLAVT achieves an excellent robustness and real-time performance compared with the PBVS baseline, benefitted from the design of complex neural network and efficient reward function. In addition, the multiple targets training adopted in this article effectively guarantees the transferability of DRLAVT by forcing the agent to learn optimal control policy with respect to motion patterns of the target.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Nowcasting of Amplitude Ionospheric Scintillation Based on Machine
           Learning Techniques

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      Authors: Otávio Carvalho;Pedro Augusto Araujo da Silva de Almeida Nava Alves;Ricardo Yvan de La Cruz Cueva;Alex Oliveira Barradas Filho;
      Pages: 4917 - 4927
      Abstract: Ionospheric scintillation is a phenomenon that can compromise and even make the operation of some space-based systems unfeasible. In this context, it is important to develop tools capable of predicting its occurrence. However, modeling this phenomenon is quite complex due to the influence of several other aspects, such as the geomagnetic and solar activities, the seasons, and the geographic location. Therefore, the main objective of this article was to develop short-term predictive models about amplitude ionospheric scintillation through machine learning techniques. The dataset used was built considering information related to geomagnetic, solar, and interplanetary activities, the phenomenon’s temporal and geographic dependence, and the ionosphere’s state. To predict the value of the scintillation index $S_{4}$ 30 min in advance, six models were used, based on three algorithms, the artificial neural network, the extreme gradient boosting, and the random forest. The results indicated a very satisfactory prediction capacity since a coefficient of determination of 0.87 was achieved by the lower performance model. Additionally, the results demonstrated the usefulness of the considered dataset and the feature selection approach in the model’s development phase, which led to better models in accord with some statistical tests performed.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Hyper-Redundant Manipulators for Operations in Confined Space: Typical
           Applications, Key Technologies, and Grand Challenges

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      Authors: Zonggao Mu;Luyang Zhang;Lei Yan;Zixuan Li;Ruichun Dong;Chengjiang Wang;Ning Ding;
      Pages: 4928 - 4937
      Abstract: In this article, we review the state of the art in hyper-redundant manipulators for applications in confined space such as on-orbit services. With their multiple degrees of freedom and slender links, hyper-redundant manipulators can offer superior dexterity and excellent operability. They can traverse freely, manipulate objects flexibly, and conform to curvilinear paths accurately in confined spaces. The by-design separation of the mechanical and electrical parts in these manipulators also offers inherent structural compliance and miniaturization. Due to the elastic characteristics of driving cables, hyper-redundant manipulators have both stiffness and flexibility. In this article, the overviews of the current state of the art in this field are provided from the perspectives of both typical applications and key technologies. We detailed the relevant studies on the configuration, obstacle avoidance, path planning, and control technologies for hyper-redundant manipulators and highlight the use of these studies in the development of practical applications. Furthermore, we propose several aspects that need to be further studied, namely efficient inverse kinematics solution, strong coupled dynamics modeling, variable stiffness control, and multiobjective trajectory optimization. Breakthroughs in these areas will provide valuable solutions for complex path planning and control of hyper-redundant manipulators.1
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Double-Layer Q-Learning-Based Joint Decision-Making of Dual
           Resource-Constrained Aircraft Assembly Scheduling and Flexible Preventive
           Maintenance

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      Authors: Qi Yan;Hongfeng Wang;
      Pages: 4938 - 4952
      Abstract: This study investigates an integrated optimization problem of aircraft assembly scheduling and flexible preventive maintenance (PM) with decision-making (FPM/DM), in which both machine flexibility and worker flexibility are considered. A mixed integer linear programming (MILP) model targeted at minimizing the assembly cycle is first established to formulate the problem. Then, CPLEX is used for solving the exact solution of the MILP model, however, it is time-consuming and difficult to apply to complex scenarios. For the tradeoff between solution quality and computational efficiency, an improved double-layer Q-learning (QL) algorithm with PM decision-making (IDLQL/PM) is further designed. Specifically, upper-layer QL is responsible for learning a proper machine selection heuristic from the given action set to ensure the machine load balance, and lower-layer QL provides a proper action (either an operation or a PM) for the selected machine to reduce unnecessary idle time and PM. Next, some instances are generated to evaluate the performance of IDLQL/PM. In a small-sized instance compared to CPLEX, the average solution gap is 1.8%, while the model solving time is reduced by 481%. As for 16 large-sized instances, IDLQL/PM presents a clear advantage over the well-known genetic algorithm and two other QL-based approaches. In addition, three state-of-the-art maintenance strategies are selected as rivals to validate the effectiveness of FPM/DM. It is observed that the proposed FPM/DM strategy can avoid improper maintenance during aircraft final assembly and ensure higher assembly efficiency.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Skeleton Extraction and Greedy-Algorithm-Based Path Planning and its
           Application in UAV Trajectory Tracking

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      Authors: Jianfang Chang;Na Dong;Donghui Li;Wai Hung Ip;Kai Leung Yung;
      Pages: 4953 - 4964
      Abstract: Space research is of great significance to increasingly decentralized and distributed space systems, and path planning in space systems has become a research hotspot for maintaining their safety, security, and reliability. To explore the passable path connecting the starting point and the target point, and optimize a smooth trajectory that can be tracked by unmanned aerial vehicles (UAVs) in 3-D space, a skeleton-extraction- and greedy-algorithm-based path planning has been proposed to guide the flight of UAVs. First, the rapidly exploring random tree (RRT) has been introduced for path search. To speed up the path search process, the spatial skeleton extraction method has been introduced to calculate the skeleton of free space The greedy algorithm has been utilized to increase the RRT expansion and reduce unnecessary bends in the path. The skeleton extraction and greedy-algorithm-based Lazy RRT and RRT-Connect have been proposed to build compared experiments. Second, the minimum snap has been applied to generate a smooth flight trajectory, and the flight time is allocated according to the distance between the waypoints. Third, the UAV Simulink model has been established, and the spatial position of the optimized trajectory is tracked. The experimental results prove that the skeleton extraction can significantly speed up the search process, and greedy algorithm can shorten the path length effectively. The minimum snap combined with the time allocation strategy can produce a smooth and feasible path. The UAV Simulink model also proves that the classic proportion-differentiation controller can accurately track the generated trajectory. The greedy algorithm and skeleton extraction reduce the average path length of RRT by 9.780 and 8.251%, respectively. The greedy algorithm and skeleton extraction reduce the average path length of RRT, Lazy RRT, and RRT-Connect by about 10–13%. The greedy algorithm and skeleton ex-raction reduce the time consumption of RRT by 62.781 and 36.276%, respectively.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Enhancing Context-Aware Reactive Planning for Unexpected Situations of
           On-Orbit Spacecraft

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      Authors: Bingqing Shen;Li Da Xu;Hongming Cai;Han Yu;Pan Hu;Lihong Jiang;Jingzhi Guo;
      Pages: 4965 - 4983
      Abstract: Spacecraft software is a complicated software system integrating many subsystems of different disciplines. Supporting on-orbit missions needs the knowledge of industrial information integration. To ensure mission success, reactive planning is a critical function in solving dynamic problems during task execution, relying on the knowledge of situation for optimal decision-making. However, it may unable to correctly identify the operational context in unexpected situations. This article solves the unexpected situation problem for reactive planning. It proposes a context-awareness model enhancement framework to identify the triggers of critical situations based on event evolution-based analysis. The framework includes the process of unexpected situation discovery, context identification, and context refinement. With this approach, preventive operational resilience can be achieved. Moreover, a symbiotic computing paradigm and a flexible inference engine are devised for addressing the on-orbit spacecraft-specific challenges. Also, an agent-based reactive system design and a system integration architecture is provided for implementing the proposed approach from both autonomy and information integration perspective. Experiment results show that the proposed approach is effective and efficient to solve the unexpected situation issue. This article offers a crucial insight to context-aware decision-support in space applications.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Integration of Light Curve Brightness Information and Layered
           Discriminative Constrained Energy Minimization for Automatic Binary
           Asteroid Detection

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      Authors: Tan Guo;Xiao-Ping Lu;Keping Yu;Yong-Xiong Zhang;Wei Wei;
      Pages: 4984 - 4999
      Abstract: Binary asteroid exploration is an important research focus in the areas of deep space exploration and space informatics because of its unique scientific value in analyzing the structure and gravitational dynamic properties of asteroids, as well as the origin and evolution of celestial bodies in the solar system. Remote photometric observations can reveal the key characteristics of binary asteroids that differ from other kinds of asteroids, especially unary asteroids, and provide an efficient and convenient way to discover binary asteroids. However, the automatic binary asteroid detection problem by advanced machine learning methodology remains unresolved when handing complex asteroid photometric data. For this problem, this article proposes to simulate unary and binary asteroid systems using cellinoid and oblate sphere shape models and generate the corresponding light curve brightness information with different asteroid physical parameters. Then, a benchmark unary and binary asteroid light curve dataset is constructed. Afterward, a layered discriminative constrained energy minimization (LDCEM) method is developed to train a binary asteroid detector in a layered learning manner by enlarging the response differences between the light curve data of unary and binary asteroids such that the two common kinds of asteroids can be well distinguished. The experimental results on the simulated asteroid light curve data, as well as the real observed asteroid light curve data, show that the proposed LDCEM method can yield promising binary asteroid detection performance in comparison with some representative detection methods.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • An Intelligent 2-D Chart Method With Autodetection for Weak Quasar Blind
           TDD Estimation in Deep Space △DOR Measurement

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      Authors: Lanhua Xia;Jifei Tang;Jun Wu;Yang Chen;Rabi Mahapatra;
      Pages: 5000 - 5014
      Abstract: To achieve high accuracy navigation for deep space probes, the △differential one-way ranging (△DOR) measurement relies on the corrections from the time delay difference (TDD) estimation of reference weak Quasar signal, which is guided by the forecasting data compensation. However, disturbed by the interference, the forecasting data sometimes is unavailable, which directly leads to invalid compensation and Quasar TDD estimation failure. In this article, an intelligent 2-D chart method with autodetection is proposed for △DOR measurement without forecasting data support. Consisted of 2-D correlation chart generation, two stages autodetection and rough estimations iteration refining, the proposed scheme realizes the autodetection and TDD blind estimations of weak Quasar signal. The validation experiment is established with developed radio science software defined receiver systems, which are installed in ground stations for space △DOR navigation and positioning. Results show that the presented 2-D correlation chart extends the system weak Quasar signal detection ability with signal SNR can reach –23 dB. The overall accuracy of designed two stages autodetection for Quasar correlation spectral line search is 85.17%. The proposed scheme improves the utilization rate of Quasar data collected by the system by 24.01%. And the average P-F curve fitting residue error which reflects the TDD estimation accuracy is 0.1931/rad. It proves an equivalent precision to accurate forecasting data processing, which meets the △DOR measurement accuracy requirements.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Automated Detection of Multitype Landforms on Mars Using a Light-Weight
           Deep Learning-Based Detector

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      Authors: Shancheng Jiang;Zongkai Lian;Kai Leung Yung;W. H. Ip;Ming Gao;
      Pages: 5015 - 5029
      Abstract: Intification of geological salient landforms is a primary requirement for spacecraft motion estimation and obstacle avoidance. As a large volume of high-resolution images are acquired by the Mars reconnaissance orbiter, growing number of approaches are proposed to develop automated approaches to detect a particular landform on Mars. However, most existing objective detection models are limited to sliding window-based and morphology-based algorithms, which require complicated preprocessing operations and can hardly be generalized to detecting different types of landforms. In this article, we aimed at developing a multitype landform detection system based on a light-weight deep learning framework, which has a quite small model size but presents excellent performance. This specific deep learning-based framework is named as mini shot multibox detector (SSD), by downsizing and modifying the existing single SSD. In the mini-SSD, some components are further optimized to adapt to this domain specific problem. A pretraining strategy is well-designed and merged into the entire model training process. In the performance evaluation tests, the proposed framework was trained and tested on images with different scales collected from different locations in high resolution imaging science experiment database. Results demonstrate that the introduced Adam optimizer and pretraining strategy can form positive effective to both model training and inference performance. The proposed framework and strategy combination outperforms the original SSD300, faster R-CNN, and YOLO series models as well as all state-of-the-art sliding window-based detectors in the field, namely AdaBoost with LBP features, AdaBoost with Haar features, and support vector machines with histogram of oriented gradient (HOG) features, in two different testing sets. Additionally, it shows high resilience on detecting target landforms in different environments with various sizes and shapes from the qualitative analysis, a-d can be generalized as a tool for relevant applications.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Semiglobal Finite-Time Stabilization of Saturated Spacecraft Rendezvous
           System by Dynamic Event-Triggered and Self-Triggered Control

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      Authors: Kai Zhang;Yang Liu;Jiubin Tan;
      Pages: 5030 - 5042
      Abstract: This article proposes the dynamic event-triggered control (ETC) and self-triggered control (STC) to achieve the semiglobal finite-time stabilization (SGFTS) of spacecraft rendezvous system with input constraints. Based on the Clohessy–Wiltshire equation, a bounded dynamic ETC algorithm is first designed, where the time-varying control gain will approach to infinity at finite-time and only be scheduled on a specified time determined by an dynamic event-triggered mechanism. This algorithm can achieve the SGFTS of the closed-loop system and save the communication resources. Moreover, the corresponding dynamic STC that only uses the data information at the previously triggered time to precompute the next triggered time is designed. By exploring the properties of the parametric Lyapunov equation, a positive minimal interevent time (MIET) of the designed dynamic ETC and STC can be obtained, such that the Zeno phenomenon is avoided. In some cases, the MIET can totally avoid the relationship with the system itself and be designed as an arbitrarily large bounded constant. Finally, simulation results verify the effectiveness of the designed algorithms.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • A Systematic Review of Machine Learning Techniques for GNSS Use Cases

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      Authors: Akpojoto Siemuri;Kannan Selvan;Heidi Kuusniemi;Petri Valisuo;Mohammed S. Elmusrati;
      Pages: 5043 - 5077
      Abstract: In terms of the availability and accuracy of positioning, navigation, and timing (PNT), the traditional Global Navigation Satellite System (GNSS) algorithms and models perform well under good signal conditions. In order to improve their robustness and performance in less than optimal signal environments, many researchers have proposed machine learning (ML) based GNSS models (ML models) as early as the 1990s. However, no study has been done in a systematic way to analyze the extent of the research on the utilization of ML models in GNSS and their performance. In this study, we perform a systematic review of studies from 2000 to 2021 in the literature that utilizes machine learning techniques in GNSS use cases. We assess the performance of the machine learning techniques in the existing literature on their application to GNSS. Furthermore, the strengths and weaknesses of machine learning techniques are summarized. In this paper, we have identified 213 selected studies and ten categories of machine learning techniques. The results prove the acceptable performance of machine learning techniques in several GNSS use cases. In most cases, the models using the machine learning techniques in these GNSS use cases outperform the traditional GNSS models. ML models are promising in their utilization in GNSS. However, the application of ML models in the industry is still limited. More effort and incentives are needed to facilitate the utilization of ML models in the PNT context. Therefore, based on the findings of this review, we provide recommendations for researchers and guidelines for practitioners.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Contextualized Recommendation of Aviation Ancillary Services Based on
           Passenger Portraits

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      Authors: Yingmin Zhang;Wenquan Luo;Min Li;Tingting Chen;
      Pages: 5078 - 5088
      Abstract: Ancillary services are crucial to Chinese domestic airlines’ profits. Personalized recommendations for ancillary aviation services must consider massive amounts of contextualized online and offline data. The issue of sparsity is more serious in massive data, which makes traditional collaborative filtering (CF) algorithms less accurate. This article adds instant contextualized travel-related factors to the modeling of air passengers’ ancillary services, thus achieving dynamic recommendations. It also proposes a four-tuple to construct a contextual ontology as a conceptual model of ancillary aviation services. The article establishes a context-aware air passenger portrait model based on four tag sets, including passenger attribute tags, ancillary service attribute tags, passenger interaction behavior tags, and contextual attribute tags. Further, it proposes a multidimensional method for calculating contextual portrait similarity; the method recommends a list of the top-N items to the target passenger according to the similarity calculation. Compared with the traditional CF recommendation method, the proposed algorithm has better performance, improving the accuracy rate by 4.3% and the recall rate by 2.62%. The final experimental result demonstrates that the contextual recommendation method achieves higher passenger satisfaction, which can provide consolidated data and decision support for the management of aviation ancillary services.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • An Integrated Collaborative Filtering Framework With Location-Aware Graph
           Embedding in Intelligent Internet of Things Systems

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      Authors: Yong Sun;Wenan Tan;Li Huang;Na Xie;Ling Ruan;Li Da Xu;
      Pages: 5089 - 5104
      Abstract: Reliable Internet of Things (IoT) service discovery is a significant task in intelligent service-oriented IoT systems. Collaborative filtering (CF) turns out to be an effective solution to IoT service discovery. However, the traditional CF framework is facing the following challenges: inefficiency in learning the high-order interactions between users and IoT services and ineffectively making use of geographical location information. Moreover, deploying a CF framework in real-world distributed IoT systems poses another significant challenge in terms of reliability assurance and privacy protection. For bridging this gap, we propose an innovative integrated collaborative filtering framework (ICF) with incorporating the location-aware quality of IoT services into a heterogeneous graph embedding model. Meanwhile, a Geohash-based privacy-preservation mechanism is introduced to encoding the location information into a short string for protecting the sensitive location information. The proposed ICF framework is an integrated architecture combining advanced graph embedding learning and heterogeneous side information. And a joint objective optimization function is designed in the graph embedding learning to qualify automatic IoT service features. In this way, the hidden user-service interaction information and location-aware quality semantic features can be explored in an effective and reliable way. Furthermore, the learned location-aware quality embedding vector is incorporated to discover the reliable services among all IoT services with the mini-batch online clustering-based CF algorithm. The experimental findings illustrate the significant efficacy and reliability of our proposed ICF framework in large scale IoT service discovery scenarios.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • NI-UDA: Graph Contrastive Domain Adaptation for Nonshared-and-Imbalanced
           Unsupervised Domain Adaptation

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      Authors: Guangyi Xiao;Weiwei Xiang;Shun Peng;Hao Chen;Jingzhi Guo;Zhiguo Gong;
      Pages: 5105 - 5117
      Abstract: With the technology development, information networks continuously generate a large amount of integrated labeled Big Data. Some types of labeled data in real scenes are scarce and difficult to obtain, such as some aerospace data. It is important to address the problem of nonshared and imbalanced unsupervised domain adaptation (NI-UDA) from the labeled Big Data with nonshared and long-tail distribution to unlabeled specified small and imbalanced space applications, where nonshared classes mean the label space out of the target domain. Previous methods proposed to integrate the semantic knowledge of Big Data to help the unsupervised domain adaptation for sparse data. However, they have the challenges of limited effect of knowledge sharing for long-tail Big Data and the imbalanced domain adaptation. To solve them, our goal is to leverage priori hierarchy knowledge to enhance domain contrastive aligned feature representation with graph reasoning. Our method consists of hierarchy graph reasoning (HGR) layer and K-positive contrastive domain adaptation (K-CDA). Our HGR contributes to learn direct semantic patterns for sparse classes by hierarchy attention in self-attention, nonlinear mapping, and graph normalization. For alleviating imbalanced domain adaptation, we proposed K-CDA, which explores k-positive instances for each class to every mini-batch with contrastive learning to align imbalanced feature representations. Compared with the previous contrastive UDA, our K-CDA alleviates the problems of large memory consumption and high computational cost. Experiments on three benchmark datasets shows our methods consistently improve the state-of-the-art contrastive UDA algorithms.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Autonomous Driving for Natural Paths Using an Improved Deep Reinforcement
           Learning Algorithm

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      Authors: Kuo-Kun Tseng;Hong Yang;Haoyang Wang;Kai Leung Yung;Regina Fang-Ying Lin;
      Pages: 5118 - 5128
      Abstract: The purpose of this article is aimed to solve the problem associated with autonomous driving on the natural paths of planets. The contribution of this work is to propose an improved deep deterministic policy gradient (DDPG) framework for the autonomous driving on natural roads requires handling uneven surface of different throttle and braking reaction speeds. Our new finding is to design an adapted DDPG algorithm by double critic and excellent experience replay as DCEER-DDPG to reduce the overestimation of state action values. In addition, we created a virtual reality environment with TORCS simulator for fair evaluation. In the experiments, the proposed DCEER-DDPG has a better performance than previous algorithms, which can improve the utilization of driving experience on a natural path and increase the learning efficiency of the strategy. For the future applications, the proposed DCEER-DDPG is used not only on Earth, but also in lunar exploration.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Multitask Allocation Framework With Spatial Dislocation Collision
           Avoidance for Multiple Aerial Robots

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      Authors: Tingjun Lei;Chaomin Luo;Timothy Sellers;Ying Wang;Lantao Liu;
      Pages: 5129 - 5140
      Abstract: Multitask allocation and trajectory planning for multiple unmanned aerial vehicles (UAVs) have been extensively used in various real-world applications. This article presents a framework of multi-UAV multitask allocation and trajectory planning with collision avoidance. The scenario of interest is one where multiple UAVs are launched in order to investigate selected targets in a massive wildfire disaster relief terrain. Initially, one UAV is launched to search wildfire locations and wildfire lines by a developed informative path planning algorithm. An informative exploratory search mechanism is developed that provides the exploration trajectories to precisely locate the wildfire positions in the wildfire environments. Afterward, with the investigated environmental information including GPS coordinates of wildfire positions and distribution as targets, UAVs are deployed to multiple target positions. In order to perform effective collision avoidance, a spatial dislocation scheme is developed by introduction of an additional dimension for UAVs at different altitudes, whereas UAVs avoid collision at the same altitude using a proposed velocity profile paradigm. Concurrent multitask allocation, trajectory planning, and collision avoidance are successfully carried out with unequal numbers of UAVs and targets. The proposed framework has been validated by simulation studies and comparative analyzes.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • MicroCrack-Net: A Deep Neural Network With Outline Profile-Guided Feature
           Augmentation and Attention-Based Multiscale Fusion for MicroCrack
           Detection of Tantalum Capacitors

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      Authors: Mingyang Cheng;Chuqiao Xu;Junliang Wang;Wenjun Zhang;Yaqin Zhou;Jie Zhang;
      Pages: 5141 - 5152
      Abstract: The microcrack defect in the terminal electrode of the tantalum capacitor seriously affects the service life of the capacitor. However, the microcrack displays low contrast and large noises in the image. Meanwhile, the width of the microcrack is small, and the area in the image is tiny, which brings additional challenges to the detection of microcracks on the terminal electrodes. To improve the performance of detecting microcracks, this article proposes a detection model with outline profile-guided feature augmentation and attention-based multiscale fusion, titled with MicroCrack-Net. In this method, the microcrack outline is utilized to guide the lossless feature extraction to strengthen unobvious microcrack features without pooling operation to avoid information loss of tiny defects during the layer-by-layer processing in the convolutional neural network. A gradual attention mechanism is proposed to attract the attention of the detection model to local defect areas in the image with noises. Multiscale feature extraction and fusion are designed to process the data in tensors of the same size. The experimental results demonstrated that the proposed model is effective and has superior performance over VGG16, resNet50, resNext50, DenseNet, Racki-Net, and SegDecNet in detecting microcracks.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • An Efficient and Privacy-Preserving Blockchain-Based Authentication Scheme
           for Low Earth Orbit Satellite-Assisted Internet of Things

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      Authors: Biying Wang;Zheng Chang;Shancang Li;Timo Hämäläinen;
      Pages: 5153 - 5164
      Abstract: Recently, integrating satellite networks (e.g., low-Earth-orbit (LEO) satellite constellation) into the Internet of Things (IoT) ecosystem has emerged as a potential paradigm to provide more reliable, ubiquitous, and seamless network services. The LEO satellite networks serves as a key enabler to transform the connectivity across industries and geographical border. Despite the convenience brought from the LEO satellite networks, it arises security concerns, in which the essential one is to secure the communication between the IoT devices and the LEO satellite network. However, some challenges inheriting from the LEO satellite networks need to be considered, which are: the dynamic topology; the resource-constraint satellites; the relative long latency; and multiple beams authentication. In particular, the centralized authentication schemes are no longer suitable for the emerging LEO satellite-assisted IoT ecosystem. In this article, we first introduce the architecture of the LEO satellite network-assisted IoT ecosystem. Then, we propose an efficient and privacy-preserving blockchain-based authentication scheme. The proposed authentication scheme takes the advantages of certificateless encryption and consortium blockchain to provide lightweight key pair computation without appealing devices’ information and efficient signature querying and verification. In addition, a fast authentication mechanism is implemented in the scheme in order to reduce the time complexity from querying a certain record for the authentication within a satellite among multiple beams. With the analysis of the storage and computation complexity, the performance evaluation demonstrates the effectiveness of the proposed scheme.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Asteroid Approaching Orbit Optimization Considering Optical Navigation
           Observability

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      Authors: Dong Qiao;Xingyu Zhou;Zeduan Zhao;Tong Qin;
      Pages: 5165 - 5179
      Abstract: An increasing demand for investigating the solar system has envisioned many asteroid exploration missions. Optical navigation is the principal technology to determine the explorer’s orbit relative to an asteroid when approaching it in an exploration mission. The approaching orbit determines the observing conditions and consequently affects the optical navigation accuracy. Aiming at improving the optical navigation performance, this article proposes the approach orbit optimization method considering optical navigation observability. The defect of the optical navigation along the line-of-sight (LOS) direction when the explorer moves along the conventional approaching orbit is revealed. The quantitative index of the navigation performance is designed by deriving the Fisher information matrix (FIM). The orbit optimization problem is constructed by involving the elements of FIM and fuel consumption together into the optimization index, and setting up the necessary engineering constraints. Numerical simulations show that compared with the conventional fuel-optimal cases, the navigation along the LOS direction is greatly improved. The navigation accuracy is improved from 86 to 99% and an additional 1.62 kg of fuel is consumed. The proposed method can effectively improve the navigation performance and has promising applications in asteroid approach trajectory design in the future asteroid exploration missions.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Resource Consumption and Radiation Tolerance Assessment for Data Analysis
           Algorithms Onboard Spacecraft

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      Authors: Gary Doran;Ameya Daigavane;Kiri L. Wagstaff;
      Pages: 5180 - 5189
      Abstract: Spacecraft operating at great distances experience limited data bandwidth and high latency for communication with Earth. Data analysis algorithms that operate onboard, the spacecraft can perform detection and discovery of events of interest without human intervention. This capability serves to increase the quality and quantity of science data collected by the mission through data summarization, downlink prioritization, and adaptive instrument mode switching. However, before such technology can be adopted for use by a mission, it is necessary to characterize the required memory and computational resources. For operation in high-radiation environments, such as in orbit around the gas giants, a characterization of radiation tolerance is also important. In this article, we propose a framework to assess the resource and radiation profiles for machine learning algorithms in a simulated spacecraft computational environment. We apply this framework to several use cases designed for the Europa Clipper spacecraft, which plans to study Jupiter’s moon Europa. This approach can also benefit other remote deployments, such as the robotic exploration of hazardous environments on Earth.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Optimal Transportation Particle Filter for Linear Filtering Systems With
           Correlated Noises

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      Authors: Jiayi Kang;Xiuqiong Chen;Yangtianze Tao;Stephen Shing-Toung Yau;
      Pages: 5190 - 5203
      Abstract: Dedicate to Professor Thomas Kailath on the occasion of his 87 Birthday. In this article, we derive an optimal transportation particle filter for linear time-varying systems with correlated noises. This method can be regarded as the extension of the feedback particle filter with an optimal transportation structure. However, the particles in our method are evolved in a deterministic way, while we need to generate random particles in a feedback particle filter. Consequently, we only need a very few particles to obtain the satisfying results, and this property is especially significant for high-dimensional problems. The error analysis of our method and the feedback particle filter has been carried out when the system is time invariant. Compared with the feedback particle filter and the ensemble Kalman filter, our method shows great efficiency in numerical experiments, including both the scalar and high-dimensional cases.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Adversarial Swarm Defence Using Multiple Fixed-Wing Unmanned Aerial
           Vehicles

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      Authors: Joonwon Choi;Minguk Seo;Hyo-Sang Shin;Hyondong Oh;
      Pages: 5204 - 5219
      Abstract: This article proposes a coverage-based adversarial swarm defence algorithm. The defender swarm composed of fixed-wing unmanned aerial vehicles (UAVs) is assumed to have explosives onboard to intercept an adversarial swarm. The proposed approach consists of the following two steps: first, impact point optimization and, second, model predictive control (MPC)-based impact time control. The impact point optimization periodically optimizes impact points for the corresponding UAVs to maximize the coverage within the hostile swarm while minimizing the common impact time. The optimization domain is limited to a physically reachable area of UAVs with the common impact time. Besides, the MPC-based impact time controller is designed to ensure the multiple UAVs to arrive the generated time-varying impact points simultaneously. Numerical simulations are performed to prove the feasibility and efficiency of the proposed swarm defence algorithm.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Energy-Efficient Initial Deployment and ML-Based Postdeployment Strategy
           for UAV Network With Guaranteed QoS

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      Authors: Kirtan Gopal Panda;Amulya Wilson;Debarati Sen;
      Pages: 5220 - 5239
      Abstract: Nowadays, the extensive use of unmanned aerial vehicle (UAV)-enabled networks in different applications demands intelligent deployment planning to utilize several benefits of UAVs. This article proposes a complete solution for deploying a UAV network over an unprecedented public meet-up area that offers a guaranteed quality-of-service demand with no interference and capacity limit violation. We call that the proposed solution is complete as it includes both initial and postdeployment planning. Under initial deployment, we offer three different placement algorithms, known as anticlockwise spiral algorithm, clockwise spiral algorithm, and hexagonal circle packing algorithm, to determine the energy-efficient 3-D positions of capacity-limited UAVs with no inter-UAV interference. After deployment, the random walk by users demands postdeployment planning for UAVs. We propose a $Q$-learning-based algorithm to realign the existing UAVs to maintain the outage. In order to do a more realistic performance assessment of the proposed algorithms, we model the user distribution for a hotspot region by the Thomas cluster point process and the Matern cluster point process. The obtained results exhibit that all three initial deployment algorithms show better performance than a random deployment. The $Q$-learning algorithm under postdeployment offers network lifetime enhancement in addition to outage improvement.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Stability Research of a Triangular Tethered Satellite Formation: Dynamics,
           Filtering, and Control

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      Authors: Bowen Su;Fan Zhang;Panfeng Huang;
      Pages: 5240 - 5255
      Abstract: This article investigates the stability of a triangular tethered satellite formation (TTSF) and the performance improvement by filtering and the virtual control of general force. The equation of motion is formulated by Lagrangian equation, and the state space equation (SSE) is derived with the second-order terms decoupled. Then the stability of SSE is discussed with the tethers nonstretched and stretched, respectively, where the existence of a stable orbit arounthe equilibrium is explored and the effect of initial conditions on the stability of the system around a stable orbit is discussed. To smooth the system response, a steepest descent-based state filter for TTSF system is designed, which provides the optimized state information. Moreover, to get the expected system response, a virtual control law is acted on the system with hysteresis relay property employed to strengthen its robustness. Finally, simulation results are shown to validate the theoretical analyses.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • An Efficient Labeled/Unlabeled Random Finite Set Algorithm for Multiobject
           Tracking

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      Authors: Thomas Kropfreiter;Florian Meyer;Franz Hlawatsch;
      Pages: 5256 - 5275
      Abstract: In this article, we propose an efficient random finite set (RFS)-based algorithm for multiobject tracking, in which the object states are modeled by a combination of a labeled multi-Bernoulli (LMB) RFS and a Poisson RFS. The less computationally demanding Poisson part of the algorithm is used to track potential objects whose existence is unlikely. Only if a quantity characterizing the plausibility of object existence is above a threshold, a new labeled Bernoulli component is created, and the object is tracked by the more accurate but more computationally demanding LMB part of the algorithm. Conversely, a labeled Bernoulli component is transferred back to the Poisson RFS if the corresponding existence probability falls below another threshold. Contrary to existing hybrid algorithms based on multi-Bernoulli and Poisson RFSs, the proposed method facilitates track continuity and implements complexity-reducing features. Simulation results demonstrate a large complexity reduction relative to other RFS-based algorithms with comparable performance.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Automatic Landing for Carrier-Based Aircraft Under the Conditions of Deck
           Motion and Carrier Airwake Disturbances

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      Authors: Haibin Duan;Lin Chen;Zhigang Zeng;
      Pages: 5276 - 5291
      Abstract: This article studies the automatic landing problem for the carrier-based aircraft in the presence of deck motion and carrier airwake disturbance. First, an automatic regressive model based prediction algorithm is proposed to generate the motion information of the deck, which in turn is used to generate corrections in the commands of the aircraft's heading and flight path to guarantee landing accuracy. Then, a fast fixed-time stable system (FFSS) is proposed. Based on the FFSS, the sliding-mode-based command differentiators are designed to estimate the first-order derivatives of the reference commands in finite time. Moreover, using the modified reaching law, the incremental sliding mode control (ISMC) is presented to design the landing guidance and control system to provide fast and accurate flight control to touchdown, where the airwake disturbances are compensated by the designed adaptive super twisting extended state observers (ASTESOs). Besides, to improve the stability during the final approach, an approach power compensation subsystem maintaining the angle of attack is proposed using the ISMC and ASTESO. The stability of the closed-loop system is analyzed using the Lyapunov theorem. Finally, comparative simulation results demonstrate the effectiveness of the proposed control scheme over state-of-the-art methods on the landing accuracy and robustness.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Data-Driven In-Orbit Current and Voltage Prediction Using Bi-LSTM for LEO
           Satellite Lithium-Ion Battery SOC Estimation

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      Authors: Seok-Teak Yun;Seung-Hyun Kong;
      Pages: 5292 - 5306
      Abstract: Accurate estimation of the battery system state of charge (SOC) is essential to the satellite mission design and fault management. However, it is difficult for low Earth orbit satellites to continuously monitor the battery SOC on the ground due to the noncontact duration. To estimate the battery SOC for the entire orbit, it is necessary to predict or monitor the battery data for all times. Therefore, existing studies use SOC estimation that relies on real-time onboard battery information or utilizes probability-based technique and power budget-based technique. The real-time onboard-based technique is unsuitable for mission design because the status information is not available to the ground during the noncontact duration. Probability-based and power budget-based techniques are not reliable during the noncontact duration. In this study, we propose the ground-based battery SOC estimation technique that predicts the current and voltage by using the bidirectional long short-term memory network for the noncontact duration and estimates the SOC by the unscented Kalman filter for all operating conditions. The proposed technique is tested with in-orbit data of the KOMPSAT-3A satellite, and we demonstrate its superior performance than other conventional ground-based SOC estimation techniques.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • QoS-Aware Software-Defined Multicast in LEO Satellite Networks

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      Authors: Menglan Hu;Ronghao Yang;Yi Hu;Chao Cai;Yan Dong;Tianping Deng;Kai Peng;
      Pages: 5307 - 5317
      Abstract: The emerging low earth orbit (LEO) broadband constellations are capable of distributing videos via advanced multicasting techniques to multiplex bandwidth in both satellite access and backbone networks, thereby reducing huge amounts of traffics. Previous work on LEO-based multicast routing relied on IP multicast, which fails to employ global information to achieve the optimal bandwidth saving. In this article, we leverage the novel software-defined multicasting paradigm on LEO constellations to empower advanced video distribution with optimized multicast trees, thereby significantly outperforming conventional multicasting techniques. In the presence of quality-of-service (QoS) constraints, we propose weighted rectilinear Steiner minimal trees (WRSTs), which balance bandwidth saving and QoS requirements. Our multicast tree algorithm adopts the Voronoi diagram and Delaunay triangulation to obtain suitable candidate Steiner points, which are exploited by subsequent iterative edge substitutions for WRST construction. We also design QoS-aware multicast management schemes to deal with frequent member updates and potential failures in LEO constellations. We carry out thorough experiments to demonstrate the effectiveness and efficiency of the proposed routing tree construction algorithm and multicasting protocols when compared with traditional algorithms.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • An Intelligent Particle Filter for Infrared Dim Small Target Detection and
           Tracking

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      Authors: Mengchu Tian;Zhimin Chen;Huifen Wang;Linyan Liu;
      Pages: 5318 - 5333
      Abstract: With consideration of low tracking accuracy and even losing target when the track-before-detect method based on particle filter (PF-TBD) tracks infrared dim small target in the complex background. In this article, a track-before-detect method based on the spring model firefly algorithm optimization particle filter (SFA-PF-TBD) was proposed to address this problem. First, the attractiveness and movement behavior of fireflies were introduced into the particle filter for optimizing the particles, and the optimization strength was controlled by evaluating the real-time distribution of particles. After optimization, detecting the density of the particles around the optimal particle, the elastic mechanism of spring was used to control the density of particles around the optimal particle when the particles gathered excessively, which made the distribution of particles more reasonable. Then, the TBD method was realized by the improved particle filter for tracking dim small target under the low signal-noise ratio conditions. Finally, we compared the SFA-PF-TBD algorithm with other algorithms by tracking experiments in simulation scenes and actual scenes. The results showed that the SFA-PF-TBD algorithm has more advantages than the PF-TBD algorithm.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • MFR—A Max-Flow-Based Routing for Future Interplanetary Networks

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      Authors: Sangita Dhara;Sujoy Ghose;Raja Datta;
      Pages: 5334 - 5350
      Abstract: Artificial satellites, space stations, landers, and rovers are continuously deployed in deep space to explore the planets’ potential resources in the solar system. Data transmission in deep space, therefore, will not be a prescheduled time/bandwidth-specific communication as it exists now. A number of sources in deep space may simultaneously transmit a vast amount of sensitive data to the Earth station (destination) using limited bandwidth and multiple hops. In this article, using a brute force approach, we first show that computing maximum flow through the nodes of a deep-space network is superexponential in nature. We evolve a number of pruning techniques to reduce the search space of the complex augmented deep-space network into trivial cases, where maximum flow (and corresponding routing) may easily be derived. In cases where it is not possible to reduce, a heuristic has been developed to find a good solution for maximizing data flow. Finally, we give a comparative simulation study and analysis between our proposed technique and the standard contact graph routing (CGR) protocol. Our algorithm outperforms CGR by a significant margin when tested in different network topologies and with various traffic generation rates at the sources.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Estimation for a Feedback System With a Desired Final State and
           Intermittent Stochastic Inputs

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      Authors: Shida Ye;Yaakov Bar-Shalom;Radu Visina;Peter Shea;Chee-Yee Chong;
      Pages: 5351 - 5360
      Abstract: The system considered in this article operates with a feedback that is characterized by a gain and a desired final state (DFS), which is the main parameter of interest in this article. The system is, however, subjected intermittently to stochastic inputs according to a Markov process. Since the system operates in two modes—under the feedback to the DFS and under a stochastic input—an interacting multiple model estimator is designed to handle the unknown DFS to be estimated (mode $M_{1}$) and the random inputs (mode $M_{2}$). Simulation results explore several scenarios and investigate the degree of observability of this stochastic problem
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Three-Dimensional Impact Angle and Time Control Guidance Law Based on
           Two-Stage Strategy

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      Authors: Chunyan Wang;Haisong Yu;Wei Dong;Jianan Wang;
      Pages: 5361 - 5372
      Abstract: In this article, a 3-D nonlinear guidance law is developed for attacking a stationary target with desired impact angle and time. The guidance law is divided into two stages—impact angle control guidance (IACG) and proportional navigation guidance (PNG). In the IACG stage, a sliding-mode guidance law is established to guarantee the satisfaction of the impact angle constraint, and in the second stage, the 3-D PNG law is applied to ensure zero miss distance. The switch between the IACG and the PNG stages is determined by a guidance parameter related to the impact time constraint. By the virtue of Newton iteration method, an appropriate guidance parameter is obtained by solving an implicit nonlinear equality after a small number of iterations. Several numerical simulations are conducted to verify the feasibility and effectiveness of the proposed guidance law under different scenarios.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • On the Use of PN Ranging With High-Rate Spectrally-Efficient Modulations
           in Satellite Payload Telemetry Links

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      Authors: Barbara Ripani;Andrea Modenini;Guido Montorsi;
      Pages: 5373 - 5381
      Abstract: Pseudonoise (PN) ranging is a ranging technique that has been recently introduced in near-Earth space research missions. For these, at the state-of-the-art, the PN ranging signal is combined with a high-rate telemetry stream binary modulated in phase. The coupling of the two, together with the 10 MHz bandwidth constraint imposed for this class of missions, translates into a data rate bound of approximately 10 Mb/s. The purpose of this article is to prove the feasibility of overcoming the current data rate limitation by defining a communication architecture that foresees the coupling of the PN ranging signal with a high-order modulated telemetry stream. To achieve the goal, we study the feasibility of pairing the PN ranging with filtered high-order modulations that are standardized for satellite payload telemetry links and investigate the simultaneous demodulation of the telemetry stream while tracking the ranging sequence. Accordingly, we design a receiver scheme capable of performing a closed-loop parallel cancellation of the ranging and the telemetry signal reciprocally. From our analysis, we find that the nonconstant envelope property, characterizing the considered modulation set, causes an additional jitter on the PN ranging timing estimation that, on the other hand, can be controlled and reduced by properly sizing the receiver loop bandwidth without limiting the timing synchronization dynamic. Our study proves the use of filtered high-order modulations combined with PN ranging to outperform the state-of-the-art in terms of spectral efficiency and achievable data rate while having comparable ranging performance.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Complex Mutual Information-Based Least-Dependent Component Analysis for
           ISAR Imaging of Multiple Targets in a Formation Flight

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      Authors: Min Kim;Kyung-Tae Kim;
      Pages: 5382 - 5392
      Abstract: This article proposes a signal decomposition-based approach for inverse synthetic aperture radar (ISAR) imaging of multiple targets in a formation. The procedure of the proposed framework is divided into three steps. The first step is to preprocess the radar echoes received by the spatial multichannel radar via pulse compression. The second step is signal decomposition using the complex-valued mutual information-based least-dependent component analysis proposed in this article. In this step, the range profiles (RPs) of multiple targets are separated into individual RPs to generate an ISAR image for each target. The third step is range-Doppler imaging using the separated RPs. As compared to the conventional methods, the proposed method decomposes the superimposed radar echoes at the raw signal level. Therefore, even if multiple targets overlap in the ISAR image, or RP histories are unaligned owing to the change in the relative positions between multiple targets, a well-focused ISAR image can still be generated. Simulation results using three targets composed of point scatterer centers verify that the proposed method can effectively segment three targets closely flying in a formation.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • A Non-Rigid Hierarchical Discrete Grid Structure and Its Application to
           UAVs Conflict Detection and Path Planning

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      Authors: Xiangyu Wu;Yi Lei;Xiaochong Tong;Yongsheng Zhang;He Li;Chunping Qiu;Congzhou Guo;Yuekun Sun;Guangling Lai;
      Pages: 5393 - 5411
      Abstract: Following the development of the unmanned aerial vehicle (UAV) industry, the number of UAVs has increased significantly, and higher expectations regarding the effect of spatial path planning have been expressed. Traditional coordinate-based conflict detection and path planning methods of UAVs are highly complex and have low computational efficiency. While the conflict detection method based on the grid system can improve efficiency, the rigid grid structure of the existing local grid system or discrete global grid system is insufficient. Since the rigid structure only encodes grid cells, multiple or different size grids are required to identify UAVs in motion, further complicating path planning. The main contributions of this article are as follows. First, we proposed a nonrigid hierarchical discrete grid structure and coding method for spatial three-dimensional grids and grid data model. This method can accomplish the aggregation of any adjacent eight grids to form multiple nonrigid structures, making the original fixed relationship between parent and child grids more flexible. Second, the nonrigid grid structure optimizes the identification ability of grid vertices, grid edges, and grid faces. It has eight times the identification ability of the rigid grid structure. We exploited this structure for UAV identification with the same size grid at any position of the grid structure. Third, based on the nonrigid grid structure, an improved UAV conflict detection method and path planning method are designed. Experimental results indicate that, on an average, the efficiency of the improved conflict detection is an order of magnitude higher than that of the traditional computational detection method. The improved path planning method reduces the path length by an average of 11% compared to the single-grid planning method, and is 3.85 times more efficient than the multi-grid buffer planning method.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Clean-to-Composite Bound Ratio: A Multipath Criterion for GNSS Signal
           Design and Analysis

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      Authors: Corentin Lubeigt;Lorenzo Ortega;Jordi Vilà-Valls;Laurent Lestarquit;Eric Chaumette;
      Pages: 5412 - 5424
      Abstract: Multipath is one of the most challenging propagation conditions affecting global navigation satellite systems (GNSS), which must be mitigated in order to obtain reliable navigation information. In any case, the random multipath nature makes it difficult to anticipate and overcome. Therefore, for legacy GNSS signal performance assessment, modern GNSS signal design, and future GNSS-based applications, robustness to multipath is a fundamental criterion. Different multipath metrics exist in the literature, such as the MPEE, usually leading to analyses only valid for a dedicated receiver/signal combination and only providing information on the bias. This article presents a general criterion to characterize the multipath robustness of a generic band-limited signal (e.g., GNSS or radar), considering the joint delay-Doppler and phase estimation. This criterion is based on the Cramér–Rao bound, which makes it universal, regardless of the receiver architecture and the signal under analysis, and provides information on the actual achievable performance in terms of estimated time-delay (i.e., pseudorange) and Doppler frequency variances.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Method of Calculating the Probability of a Safe Landing for Ship-Based
           Aircraft

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      Authors: Sergei Semakov;Ivan Semakov;
      Pages: 5425 - 5442
      Abstract: We propose the method for calculating the probability of a safe landing for ship-based aircraft. We define a safe landing as the event that the initial touch of the landing surface by an aircraft occurs on a given segment of the deck, and at the time of this contact, the phase coordinates of the aircraft (elevation angle, banking angle, vertical velocity, and so on) are within the specified limits. We propose a formula for estimating the desired probability and a formula for determining the maximum possible error $Delta P$ of this estimate: If $ P$ is an unknown exact value of the desired probability and $hat{P}$ is an approximate calculated value, then $ P-hat{P} leq Delta P$. We implement the method on the example of the automatic landing of a MiG-29 K aircraft on the aircraft carrier “Admiral Kuznetsov.” Random perturbations are caused by an atmospheric turbulence and ship’s motions. We present and discuss the calculation results. These results show that the error $Delta P$ is negligible, so that the proposed formula for $hat{P}$ determines the desired probability $P$ almost exactly.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Multistatic Passive Radar Target Detection Under Uncalibrated Receivers
           With Direct-Path Interference

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      Authors: Amir Zaimbashi;Maria Sabrina Greco;
      Pages: 5443 - 5455
      Abstract: This article examines the multistatic passive radar target detection problem under uncalibrated receivers with direct-path interference. To this aim, we consider a passive multistatic radar consisting of several transmitters nonoverlapping in frequency with a common bandwidth and several spatially separated receivers without exploiting the reference channel. For uncalibrated, multichannel and multifrequency receivers and in the presence of direct-path interference signal, we model the target detection problem as a composite hypothesis testing problem and apply the Rao framework to obtain a closed-form test statistic. Monte Carlo simulation results are provided to illustrate the performance of the proposed Rao detector and compare it with several well-known passive radar target detection algorithms. The results show that the proposed detector significantly outperforms its counterparts for different multistatic configurations in terms of detection probability and false alarm regulation as well.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Estimation of Consistent Cross-Covariance Matrices in a Multisensor Data
           Fusion

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      Authors: Carlo Quaranta;Giorgio Balzarotti;
      Pages: 5456 - 5469
      Abstract: The article investigates the problems relevant to the fusion of data provided by sensors used for detection and tracking. One of the most burdensome problems on any data fusion in these fields of application is the search for the appropriate correlation function between the estimation errors of the tracks from the various sensors. The absence of such a function can lead to excessively optimistic cross-covariance matrices between tracks with consequent inconsistencies and missed associations. The article has the aim to provide an effective solution to this problem. The proposed technique resides to build consistent cross-covariance matrices by means of a tracking system which uses as input the simultaneous associations of the tracks of the involved sensors. Furthermore, a fusion equation, that provides an unbiased and optimal global estimate in the sense of the minimum mean square error, is proposed. The method results simple and fast as well as precise. Finally, the robustness of the algorithms is proved through dedicated simulations.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • An Efficient Architecture and High-Throughput Implementation of
           CCSDS-123.0-B-2 Hybrid Entropy Coder Targeting Space-Grade SRAM FPGA
           Technology

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      Authors: Panagiotis Chatziantoniou;Antonis Tsigkanos;Dimitris Theodoropoulos;Nektarios Kranitis;Antonis Paschalis;
      Pages: 5470 - 5482
      Abstract: Nowadays, hyperspectral imaging is recognized as cornerstone remote sensing technology. The explosive growth in image data volume and instrument data rates, compete with limited on-board storage and downlink bandwidth, making hyperspectral data compression a mission critical task. Recently, the consultative committee for space data systems (CCSDS) extended the previous issue of the CCSDS-123.0 Recommended Standard for multi/hyperspectral image compression to provide Near-Lossless compression functionality. A key feature of the CCSDS-123.0-B-2 is the improved Hybrid Entropy Coder providing substantially better compression performance than the Issue 1 entropy coders at low bit rates. In this paper, we introduce a high-throughput hardware implementation of the CCSDS-123.0-B-2 Hybrid Entropy Coder. The introduced architecture exploits the systolic design pattern providing modularity and latency insensitivity in a deep and elastic pipeline, achieving constant throughput of 1 sample/cycle with small field programmable gate array (FPGA) resource footprint. This architecture is described in portable VHDL register-transfer level (RTL) and implemented, validated and demonstrated on a commercially available Xilinx KCU105 development board hosting a Xilinx Kintex Ultrascale XCKU040 SRAM FPGA, and thus, is directly transferable to the Xilinx Radiation Tolerant Kintex UltraScale XQRKU060 space-grade devices. Moreover, state-of-the-art SpaceFibre (ECSS-E-ST-50-11C) serial link interface and test equipment were used in the validation platform to emulate an on-board deployment. The introduced CCSDS-123.0-B-2 Hybrid Entropy Coder achieves a constant throughput performance of 305 MSamples/s. To the best of our knowledge, this is the first published fully-compliant architecture and high-throughput implementation of the CCSDS-123.0-B-2 Hybrid Entropy Coder, targeting space-grade FPGA technology.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Optimal Projected Circular Orbit for Spacecraft Formation Flying Near a
           Slowly Rotating Asteroid

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      Authors: Wei Wang;Di Wu;Ran Sun;Hexi Baoyin;
      Pages: 5483 - 5493
      Abstract: In spacecraft formation flying missions, the projected circular (relative) orbit (PCO), which appears circular when viewed along the zenith-nadir direction of the nominal chief spacecraft, is suitable for constructing a synthetic aperture radar. In this paper, the analysis of a PCO for spacecraft formation flying near a slowly rotating asteroid is conducted. By matching the in-plane and out-of-plane relative motion frequency, the critical values of the initial phase angle that characterizes the PCO are identified, resulting in the minimum precession of relative orbit. The analysis is then utilized to develop the fuel-optimal control law for establishing a target PCO. In particular, the fuel-optimal problem is addressed with an indirect method, where a homotopy approach is adopted, and the initial (unknown) costate vector is calculated with a scaling technique, so that the sensitivity to the initial guess problem is mitigated.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Robust ISAR Target Recognition Based on ADRISAR-Net

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      Authors: Xuening Zhou;Xueru Bai;Li Wang;Feng Zhou;
      Pages: 5494 - 5505
      Abstract: Due to the inherent unknown image deformation among the training and test samples, performance of the deep convolutional neural network (CNN) will be degraded for Inverse Synthetic Aperture Radar (ISAR) automatic target recognition. Meanwhile, traditional CNN only captures the local spatial information due to small receptive fields, thus, neglects the global information useful for recognition. To tackle these issues, this article proposes the attention-augmented deformation robust ISAR image recognition network, dubbed as ADRISAR-Net. The model adopts the inverse compositional spatial transformer for automatic image deformation adjustment, and performs joint local and global feature extractions by the attention-augmented CNN. Finally, the softmax classifier outputs the recognition results. The proposed ADRISAR-Net is end-to-end trainable, and achieves higher recognition accuracy for the four-satellite and three-airplane ISAR image data sets generated by electromagnetic computing.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Vision-Based Nonlinear Incremental Control for a Morphing Wing With
           Mechanical Imperfections

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      Authors: Bo Sun;Tigran Mkhoyan;Erik-Jan van Kampen;Roeland De Breuker;Xuerui Wang;
      Pages: 5506 - 5518
      Abstract: Morphing structures have acquired much attention in the aerospace community because they enable an aircraft to actively adapt its shape during flight, leading to fewer emissions and fuel consumption. Researchers have designed, manufactured, and tested a morphing wing named SmartX-Alpha, which can actively alleviate loads while achieving the optimal lift distribution. However, the widely existing mechanical imperfections can degrade the performance of the morphing wing and even lead to instabilities. To tackle these issues, this article proposes a vision-based adaptive control approach to actively compensate for mechanical imperfections. In this approach, an incremental model is constructed online to identify the system dynamics using servo commands and vision measurements, and then, nonlinear dynamic inversion control is applied based on the identified model. This data-driven control approach with visual feedback has been validated by real-world experiments on the SmartX-Alpha. The results demonstrate that the vision-based system combined with the proposed control methodology can actively compensate for mechanical imperfections with minimal adjustments to the actual system design. Compared to a controller that only uses a feedforward input-output mapping, this proposed approach improves the system performance and decreases the tracking errors by more than 62% despite disturbances. The results collectively demonstrate the effectiveness of the proposed control system, which sets a foundation for realizing morphing in next-generation aircraft.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Least-Squares Equalizer Demonstrations Using a Full-Digital Bandwidth
           Sub-Nyquist-Sampled Wideband Beamformer on an RFSoC

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      Authors: Kyle Steiner;Mark Yeary;
      Pages: 5519 - 5532
      Abstract: The radio frequency system-on-a-chip (RFSoC) has recently become a viable candidate for completely replacing traditional analog and digital front ends, facilitating the development of wideband phased-array systems where the modern-day RFSoC takes the comprehensive, dominate role in the architecture of the array. Wideband phased-array systems require high-fidelity compensation techniques capable of correcting imbalanced and dispersive channel effects for effective beamforming. This article provides solutions to these challenges by designing a wideband equalizer for a sub-Nyquist-sampled 1.6-GHz S-band phased-array system implemented on a Xilinx 8-channel RFSoC, whose analog-to-digital converters (ADC) operate at 4 gigasamples per second. In brief, an RFSoC is a unique, state-of-the-art, highly integrated device that incorporates a field programmable gate array, high-speed ADCs and digital-to-analog converters with a system-on-a-chip architecture on a single monolithic device. By definition, true time delay beamsteering can be implemented digitally via a combination of integer-sample delays and fractional-sample delay finite impulse response filters. By modifying the filter structure of the fractional-sample delay filter bank to support complex coefficients, channel equalization is integrated with fractional-sample delay filters to mitigate undesired channel effects. For the first time, to the best of our knowledge, this article has developed an equalizer design methodology for an uncalibrated 8-element RFSoC-based sub-Nyquist-sampled wideband beamformer. Lab measurements confirm efficacy.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Neural Fictitious Self-Play for Radar Antijamming Dynamic Game With
           Imperfect Information

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      Authors: Kang Li;Bo Jiu;Wenqiang Pu;Hongwei Liu;Xiaojun Peng;
      Pages: 5533 - 5547
      Abstract: One emerging issue in modern electronic warfare is the competition between the radar and jammer, which in principle can be viewed as a noncooperative game with two players. In practice, the interaction between the radar and jammer involves multiple rounds as well as partial observation. This makes the competition become a dynamic game with imperfect information. Antijamming strategy design for such kind of a game is still unclear. In this work, the competition between a frequency agile radar and a transmit/receive time-sharing jammer is considered. We utilize an extensive-form game (EFG) with imperfect information to model the multiple rounds interaction between the radar and jammer. For the established EFG, finding Nash equilibrium (NE) strategies is a computationally-intractable task since the number of information states grows exponentially with respect to game stages. Instead, a sampled-based learning method called neural fictitious self play algorithm is used to find approximate NE strategies (ANES). Simulation results show that ANES can be obtained and outperform the common elementary and advanced strategies from the perspective of detection performance.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Spatio-Temporal-Frequency Graph Attention Convolutional Network for
           Aircraft Recognition Based on Heterogeneous Radar Network

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      Authors: Han Meng;Yuexing Peng;Wenbo Wang;Peng Cheng;Yonghui Li;Wei Xiang;
      Pages: 5548 - 5559
      Abstract: This article proposes a knowledge- and data-driven graph neural network-based collaboration learning model for reliable aircraft recognition in a heterogeneous radar network. The aircraft recognizability analysis shows that the semantic feature of an aircraft is motion patterns driven by the kinetic characteristics, and the grammatical features contained in the radar cross-section (RCS) signals present spatial–temporal-frequency (STF) diversity decided by both the electromagnetic radiation shape and motion pattern of the aircraft. Then, an spatio-temporal-frequency graph attention convolutional network (STFGACN) is developed to distill semantic features from the RCS signals received by the heterogeneous radar network. Extensive experiment results verify that the STFGACN outperforms the baseline methods in terms of detection accuracy, and ablation experiments are carried out to further show that the expansion of the information dimension can gain considerable benefits to perform robustly in the low signal-to-noise ratio region.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Attitude Control and Stability Analysis of Electric Sail

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      Authors: Chonggang Du;Zheng H. Zhu;Junjie Kang;
      Pages: 5560 - 5570
      Abstract: This article investigates the attitude control and stability analysis of an electric solar wind sail (E-sail) by considering elastic deflection of tethers while assuming main spacecraft and remote units as point masses. The attitude and orbital motion of the E-sail is analyzed by a high-order high-fidelity E-sail model derived from the nodal position finite-element method, where the attitude angles are implicitly described via the nodal coordinates. To overcome the difficulty in handling the stability analysis of high-order model under the Lyapunov framework, the E-sail's attitude dynamics is approximated explicitly by a reduced order analytical model with only three attitude angles. A sliding mode control law is proposed for the E-sail attitude control based on the reduced order analytical E-sail model and its stability is proved by the Lyapunov theory. Finally, two schemes are derived to map the control torque to either the control thrust at remote units or the voltages of main tethers respectively, which are applied to the high-fidelity E-sail model for attitude control. Numerical simulation demonstrates that the proposed control law performs similarly with the high-fidelity and reduced order analytical E-sail models if proper control gains are selected. It shows that the control law developed from the reduced order analytical E-sail model can stably control the attitude of a real E-sail. The investigation also indicates that the high-order flexible E-sail model provides an effective virtual testbed to evaluate the E-sail attitude control strategy derived from the reduced order attitude dynamics.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Direction of Arrival Estimation and Phase-Correction for Noncoherent
           Subarrays: A Convex Optimization Approach

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      Authors: Tom Tirer;Oded Bialer;
      Pages: 5571 - 5585
      Abstract: Estimating the direction of arrival (DOA) of sources is an important problem in aerospace and vehicular communication, localization, and radar. In this article, we consider a challenging multisource DOA estimation task, where the receiving antenna array is composed of noncoherent subarrays, i.e., subarrays that observe different unknown phase shifts at every snapshot (e.g., due to waiving the demanding synchronization of local oscillators across the entire array). We formulate this problem as the reconstruction of joint sparse and low-rank matrices and solve the problem’s convex relaxation. To scale the optimization complexity with the number of snapshots better than general-purpose solvers, we design an optimization scheme, based on integrating the alternating direction method of multipliers and the accelerated proximal gradient techniques, which exploits the structure of the problem. While the DOAs can be estimated from the solution of the aforementioned convex problem, we further show how an improvement is obtained if, instead, one estimates from this solution only the subarrays’ phase shifts. This is done using another, computationally light, convex relaxation that is practically tight. Using the estimated phase shifts, “phase-corrected” observations are created and a final plain (“coherent”) DOA estimation method can be applied. Numerical experiments show the performance advantages of the proposed strategies over existing methods.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • A Review and Analysis of Attack Vectors on MIL-STD-1553 Communication Bus

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      Authors: Karim Lounis;Ziad Mansour;Michael Wrana;Marwa A. Elsayed;Steven H. H. Ding;Mohammad Zulkernine;
      Pages: 5586 - 5606
      Abstract: MIL-STD-1553 has been used for the past four decades by the military as a standardized, reliable, and fault-tolerant communication bus to provide connectivity between different embedded components in mission-critical military vehicles. The bus was designed with a great focus on reliability, responsiveness, and fault tolerance. However, its security aspects were an afterthought. Indeed, in the early 1970s, the notion of cyberattacks was not ubiquitous as it is today. Attacking computerized systems located at very high altitudes was an inconceivable scenario for many people, including security engineers. With current developments in cybersecurity and telecommunication networks, the security analysis of the MIL-STD-1553 bus reveals that the system is not immune from cyberattacks. The bus is vulnerable to many attacks that could seriously damage the entire system. Rebuilding the security of MIL-STD-1553 from scratch is cost prohibitive and a very complex, not scalable, and inflexible approach. A common alternative to embedding security to the existing system is the development of an intrusion detection system that can be added to the MIL-STD-1553 bus with minimal cost. In this article, we review and discuss some possible attack vectors on the MIL-STD-1553 bus. Then, we analyze the risk and consequences of each attack vector on a fighter jet. This review and analysis will provide security engineers with a holistic overview of possible attacks and their related risk on MIL-STD-1553 to better design an effective intrusion detection system.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Flight Demonstration of Net Electric Charge Control of Aircraft Using
           Corona Discharge

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      Authors: Benjamin C. Martell;Pol Fontanes;Joan Montanyà;Carmen Guerra-Garcia;
      Pages: 5607 - 5618
      Abstract: This article reports on a flight experiment demonstrating the ability to control the net electrical charge of a small unmanned aircraft using ion emission from a corona discharge. The charging mechanism is based on the advection of ions produced by the corona discharge, leaving charge of the opposite polarity on the aircraft. An onboard high voltage power supply of up to $pm$15 kV was remotely operated to control the level of charging. The high voltage terminal of the power supply was connected to two thin corona wires placed above the aft surface of the wing in the spanwise direction, whereas the low voltage terminal was connected to the conductive airframe. Using this strategy, electric potentials of up to −30 and +23 kV relative to the environment were acquired by the aircraft, using positive and negative corona discharges, respectively. The study addresses the dependencies with power supply bias and wind speed. Possible applications of net charge control include risk reduction of aircraft-triggered lightning strikes, as explored by the authors in prior work, as well as a means of compensating for precipitation static and other sources of charging.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • A Task Modeling Method of Intelligent Human–Computer Interaction in
           Aircraft Cockpits Based on Information Load Flow

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      Authors: Xia Zhang;Youchao Sun;Yanjun Zhang;
      Pages: 5619 - 5634
      Abstract: Defining how tasks should be performed is an essential prerequisite for designing the next-generation aircraft cockpits with multimodal intelligent human–computer interaction (HCI) modes. To this end, a task modeling method based on information load flow is proposed in this article, namely weighted social network analysis (WSNA). In the WSNA method, the node relation complexity in a weighted social network is quantified based on entropy, and composite matrices are established to describe the association frequency and strength between linked nodes. The performance evaluation indicators are proposed to measure the quality of weighted social networks on node centrality, node strength, network density, and network robustness, which contributes to identify the overload nodes and edges. The transformation rules from traditional modes to intelligent modes are also created as guidelines for application. A case study set in an HCI scenario during the approach phase is given for illustration. The information load and overall performance are compared in these two task modes. The results verify the feasibility of WSNA and thereby provide a solution to planning the HCI tasks in next-generation aircraft cockpits.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Continuous Nonsingular Terminal Sliding-Mode Control With Integral-Type
           Sliding Surface for Disturbed Systems: Application to Attitude Control for
           Quadrotor UAVs Under External Disturbances

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      Authors: Ngo Phong Nguyen;Hyondong Oh;Jun Moon;
      Pages: 5635 - 5660
      Abstract: This article proposes a continuous nonsingular terminal sliding-mode control with integral-type sliding surface (CNTSMC-ISS) framework for disturbed systems, in which we consider two types of finite-time controllers: the state feedback CNTSMC-ISS and the disturbance-observer-based CNTSMC-ISS. Compared with the existing sliding-mode controllers, the noteworthy contributions of two finite-time controllers in the proposed CNTSMC-ISS framework are the alleviation of the chattering phenomenon, the fast finite-time stability, and the singularity-free and ease-of-implementation characteristics. In the proposed CNTSMC-ISS framework, we first introduce a nonsingular integral terminal sliding-mode surface (NITSMS) such that the finite-time convergence of the system states to zero in the sliding phase is ensured and the singularity problem is avoided. Besides, a finite-time observer is developed to recover the external disturbance. Then, based on the constructed NITSMS with the supertwisting-like algorithm, the state feedback CNTSMC-ISS and the disturbance-observer-based CNTSMC-ISS are proposed, which generate the continuous control signals and guarantee the fast finite-time convergence of the system states to the designed sliding surface. Rigorous finite-time stability of the closed-loop system under two proposed controllers is provided. Finally, we apply two finite-time controllers in the proposed CNTSMC-ISS framework to the attitude control for quadrotor unmanned aerial vehicles under external disturbances. Extensive simulation and experimental results are illustrated to prove the effectiveness of the proposed attitude controllers.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • A Bayesian Network for the Classification of Human Motion as Observed by
           Distributed Radar

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      Authors: Peter Svenningsson;Francesco Fioranelli;Alexander Yarovoy;Anthony F. Martone;
      Pages: 5661 - 5674
      Abstract: In this article, a statistical model of human motion as observed by a network of radar sensors is presented where knowledge on the position and heading of the target provides information on the observation conditions of each sensor node. Sequences of motions are estimated from measurements of instantaneous Doppler frequency, which captures informative micromotions exhibited by the human target. A closed-form Bayesian estimation algorithm is presented that jointly estimates the state of the target and its exhibited motion class which are described by a hidden Markov model. To correct errors in the estimated motion class distribution introduced by faulty modeling assumptions, calibration of the probability distribution and measurement likelihood is performed by isotonic regression. It is shown, by modeling sensor observation conditions and by isotonic calibration of the measurement likelihood that a cognitive resource management system is able to increase classification accuracy by 5${%}$–10${%}$ while utilizing sensor resources in accordance with defined mission objectives.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • PointNet+LSTM for Target List-Based Gesture Recognition With Incoherent
           Radar Networks

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      Authors: Nicolai Kern;Timo Grebner;Christian Waldschmidt;
      Pages: 5675 - 5686
      Abstract: Radar-based gesture recognition can provide autonomous electronic systems with a reliable way to infer a human's intention, e.g., in traffic environments involving vulnerable road users. Particularly in complex scenarios, algorithms operating on radar target lists derived from constant false-alarm rate outputs present an attractive solution, as they not only enable the filtering of relevant targets, but can also make full use of the diverse, high-resolution target parameters provided by modern radar sensors. Therefore, this article proposes PointNet+long short-term memory (LSTM) for the enhanced target list-based recognition of challenging traffic gestures, combining per-frame feature extraction with PointNet and learning from sequences with a LSTM. The approach is generalized to facilitate the use of multistatic radar data from sensor networks to exploit slightly different viewing angles, which is particularly helpful for motions with low radial velocity. The proposed method is validated on a comprehensive dataset comprising eight traffic gestures and data recorded from 35 participants. Measurements are conducted both indoors and outdoors with an incoherent radar sensor network comprising three chirp sequence–multiple-input multiple-output sensors. On this challenging dataset, our approach clearly outperforms a reference convolutional neural network, reaching up to $92.2 %$ cross-validation accuracy.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Turret–Runner–Penetrator Differential Game With Role
           Selection

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      Authors: Alexander Von Moll;Daigo Shishika;Zachariah Fuchs;Michael Dorothy;
      Pages: 5687 - 5702
      Abstract: A scenario is considered in which two cooperative Attackers aim to infiltrate a circular target guarded by a Turret. The engagement plays out in the two-dimensional plane; the holonomic Attackers have the same speed and move with simple motion and the Turret is stationary, located at the target circle’s center, and has a bounded turn rate. When the Turret’s look angle is aligned with an Attacker, that Attacker is neutralized. In this article, we focus on a region of the state space, wherein only one of the Attackers is able to reach the target circle—and even then, only with the help of its partner Attacker. The Runner distracts the Turret until it is neutralized, which allows the Penetrator to gain a positional advantage and guarantee success in hitting the target circle. We formulate the Turret–Runner–Penetrator scenario as a differential game over the value of the subsequent game of min/max terminal angle, which takes place between the Turret and Penetrator once the Runner has been neutralized. The solution to the game of degree, including equilibrium Turret, Runner, and Penetrator strategies, as well as the Value function is given. The case in which the Penetrator can reach the target before the Turret can neutralize the Runner is formulated and solved. Finally, the assumption of a priori defined roles/goals is relaxed and the minimum of the solutions to the two fixed-role games is shown to be a global stackelberg equilibrium (GSE).
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • DOA Estimation of Mixed Circular and Noncircular Sources Using Nonuniform
           Linear Array

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      Authors: Xue Zhang;Zhi Zheng;Wen-Qin Wang;Hing Cheung So;
      Pages: 5703 - 5710
      Abstract: In this article, an algorithm for accurately estimating the directions-of-arrival (DOAs) of mixed circular and noncircular signals with a nonuniform linear array is devised. Via applying coarray interpolation on the difference and sum coarrays, we first formulate two nuclear norm minimization problems to reconstruct an augmented covariance matrix and an augmented unconjugated covariance matrix, respectively. Based on the reconstructed matrices, we further build the extended covariance matrix in the coarray domain. Finally, we construct several spectral functions to successively estimate the DOAs of the nonstrictly noncircular, strictly noncircular, and circular signals. Unlike the existing schemes, the proposed algorithm can identify accurately circular, strictly noncircular, and nonstrictly noncircular signals. Numerical results demonstrate the superiority of the proposed algorithm over existing techniques.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Ballistic Target Recognition Based on 4-D Point Cloud Using Randomized
           Stepped Frequency Radar

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      Authors: Chuncheng Zhao;Lei Wang;Yimin Liu;
      Pages: 5711 - 5729
      Abstract: Ballistic target recognition (BTR) is critical to the ballistic missile defense system. The challenge of this task is to distinguish warheads from numerous unknown confusing targets within a short observing time. The micromotion feature is proved to be effective for this task. However, traditional methods need a long observing time to acquire enough information for the recognition because of using low-dimensional features. In addition, these model-driven methods cannot handle irregular ballistic targets, such as debris. In this article, we propose a BTR scheme, which characterizes the micromotion features with a higher dimensional representation, i.e., the time–range–velocity–power 4-D point cloud, using the randomized stepped frequency radar. The higher dimensional information contained in the 4-D point cloud can reduce the required observing time. Besides, this scheme combines the model-driven method with a data-driven deep neural network to meet the challenge of model mismatch caused by irregular targets. As a result, the proposed BTR scheme is time efficient and robust, which has been proved on an electromagnetic simulation dataset.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Super-Resolution TOA and AOA Estimation for OFDM Radar Systems Based on
           Compressed Sensing

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      Authors: Min Wu;Chengpeng Hao;
      Pages: 5730 - 5740
      Abstract: This article presents a compressed sensing-based time of arrival (TOA) and angle-of-arrival (AOA) estimation algorithm for orthogonal frequency division multiplexing (OFDM) radar systems. The algorithm is designed for noncooperative targets based on a uniform linear array using a cyclic prefix (CP) added OFDM signal. The algorithm makes three key technical contributions. First, the algorithm adopts the CP-based OFDM signal for the radar TOA/AOA estimation to suppress the multitarget interference and the impact of time delay on the subcarrier orthogonality. Second, this article exploits the structure of the CP-OFDM radar signal model to construct the optimization problem of the joint TOA/AOA recovery. The super-resolution AOA estimation is obtained by using a redundant dictionary containing much more basis than the number of antennas. Third, the algorithm proposes an efficient way to solve the optimization by utilizing the properties of the circulant matrix, fast Fourier transform, and Hadamard multiplication. The simulation results indicate the effectiveness of the proposed algorithm.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Repeat Ground-Track Orbit Design Using Objective Dimensionality Reduction
           and Decoupling Optimization

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      Authors: Tan Ju;Xiaowei Shao;Dexin Zhang;Xiaofang Wei;
      Pages: 5741 - 5748
      Abstract: It is important for China's Land Observing Satellite-1 to run in a repeat ground-track orbit with almost constant altitude over the same geographical area, so the satellite can have a high-precision deformation monitoring capability. The strong continuity of reference trajectory and high computational efficiency of the algorithm are required in the satellite orbit design stage. To fulfill those requirements, we first propose a fast orbit design method based on objective dimensionality reduction and decoupling optimization, which makes the satellites drift naturally without additional propellant consumption. Besides, a completely different convergence standard for the orbit design is introduced, which greatly reduces the complexity of the optimization process by decreasing the optimization objective dimension. Meanwhile, an optimal strategy keeping the argument of latitude constant is proposed, which reduces the coupling characteristics among optimization objectives. In addition, the optimization model of the repeat ground-track orbit is established. Then, the steepest descent method is introduced to solve this optimization problem. Simulation results in the high-order gravity force reveal that the position accuracy and velocity accuracy of the reference trajectory are up to millimeters and centimeters per second respectively. The calculation efficiency of our method is about 56 times faster than that of the NSGA-II method.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Fully Adaptive Radar for Multiple Target Tracking

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      Authors: Peter John-Baptiste;Kristine L. Bell;Joel Tidmore Johnson;Graeme Edward Smith;
      Pages: 5749 - 5765
      Abstract: This work focuses on the development of a fully adaptive radar multiple target tracking (FAR-MTT) model and demonstrates its benefit over static parameter selections. The FAR-MTT framework is an extension of the fully adaptive radar (FAR) framework for single-target tracking (STT). In transitioning FAR-STT concepts to FAR-MTT, the focus was on developing a multiple target Fisher information matrix to capture the effects of multiple targets on the radar measurements, and on developing a robust but straightforward optimization scheme/objective function in the FAR executive processor. Simulation and experimental examples are presented to demonstrate the performance of the FAR-MTT approach. Compared to the fixed parameter case, the FAR-MTT technique is capable of obtaining specified tracking performance for each target and ensuring target separation while reducing sensor resource usage in a variety of multiple target environments. In Monte Carlo testing the method resulted in track merges 28% of the time compared with 50% of the time for a static parameter approach.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Dynamic Event-Triggered Antidisturbance Control for Flexible Spacecraft
           Systems

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      Authors: Yawen Wang;Haibin Sun;Linlin Hou;
      Pages: 5766 - 5783
      Abstract: This study develops a dynamic event-triggered anti-disturbance (DETAD) controller to solve attitude control problem for flexible spacecraft systems with multiple disturbances. The disturbances involve two aspects: one belongs to $L_{2}$ space, and the other is described by an exogenous nonlinear system. Two sets of Takagi–Sugeno (T–S) fuzzy models are employed to model the flexible spacecraft systems and an exogenous nonlinear system, respectively. Moreover, a flexible vibration observer (FVO) is introduced to estimate the flexible modes. And a fuzzy disturbance observer (FDO) is designed to estimate the T–S fuzzy modeling disturbance in the case of the unmeasurable premise variables. By utilizing the estimations of two observers, a DETAD controller is constructed, where a dynamic event-triggered mechanism is implemented to reduce communication transmission. Additionally, new sufficient conditions for the closed-loop system to be asymptotically stable with strict dissipative performance are formed using linear matrix inequality techniques. Simulations are also provided to verify the advantages of the proposed scheme for flexible spacecraft systems subject to different types of disturbances.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • State-Following-Kernel-Based Online Reinforcement Learning Guidance Law
           Against Maneuvering Target

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      Authors: Chi Peng;Hanwen Zhang;Yongxiang He;Jianjun Ma;
      Pages: 5784 - 5797
      Abstract: In this article, a state-following-kernel-based reinforcement learning method with an extended disturbance observer is proposed, whose application to a missile-target interception system is considered. First, the missile-target engagement is formulated as a vertical planar pursuit–evasion problem. The target maneuver is then estimated by an extended disturbance observer in real time, which leads to an infinite-horizon optimal regulation problem. Next, utilizing the local state approximation ability of state-following kernels, the critic neural network (NN) and actor NN for synchronous iteration are constructed to calculate the approximate optimal guidance policy. The states and NN weights are proven to be uniformly ultimately bounded using the Lyapunov method. Finally, numerical simulations against different types of nonstationary targets are effectively tested, and the results highlight the role of state-following kernels in the value function and policy approximation.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Universal Learning Waveform Selection Strategies for Adaptive Target
           Tracking

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      Authors: Charles E. Thornton;R. Michael Buehrer;Harpreet S. Dhillon;Anthony F. Martone;
      Pages: 5798 - 5814
      Abstract: Online selection of optimal waveforms for target tracking with active sensors has long been a problem of interest. Many conventional solutions utilize an estimation-theoretic interpretation, in which a waveform-specific Cramér–Rao lower bound on measurement error is used to select the optimal waveform for each tracking step. However, this approach is only valid in the high SNR regime, and requires a rather restrictive set of assumptions regarding the target motion and measurement models. Furthermore, due to computational concerns, many traditional approaches are limited to near-term, or myopic, optimization, even though radar scenes exhibit strong temporal correlation. More recently, reinforcement learning has been proposed for waveform selection, in which the problem is framed as a Markov decision process, allowing for long-term planning. However, a major limitation of reinforcement learning is that the memory length of the underlying Markov process is often unknown for realistic target and channel dynamics, and a more general framework is desirable. This work develops a universal sequential waveform selection scheme which asymptotically achieves Bellman optimality in any radar scene, which can be modeled as a $U{text{th}}$-order Markov process for a finite, but unknown, integer $U$. Our approach is based on well-established tools from the field of universal source coding, where a stationary source is parsed into variable length phrases in order to build a context-tree, which is used as a probabilistic model for the scene’s behavior. We show that an algorithm based on a multialphabet version of the context-tree weighting method can be used to optimally solve a broad class of waveform-agile tracking problems while making minimal assump-ions about the environment’s behavior.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Angle Estimation for Bistatic MIMO Radar Using One-Bit Sampling Via Atomic
           Norm Minimization

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      Authors: Zhi Zheng;Ning Guo;Wen-Qin Wang;
      Pages: 5815 - 5822
      Abstract: In this article, we focus on the bistatic multiple-input multiple-output (MIMO) radar that performs one-bit quantization of received echo data. Based on the one-bit bistatic MIMO radar, we devise an effective algorithm for joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation. By exploiting the signal sparsity, we first formulate a decoupled atomic norm minimization problem to reconstruct two noise-free covariance matrices from one-bit quantized data. Subsequently, we apply the MUSIC algorithm on two reconstructed matrices to estimate the DODs and DOAs of targets, respectively. Furthermore, an additional procedure is proposed to achieve pairing of the estimated DODs and DOAs. The proposed algorithm can achieve satisfactory estimation performance only using single-pulse data. Numerical results demonstrate the effectiveness of the proposed algorithm.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Spacecraft Proximity Maneuvering and Rendezvous With Collision Avoidance
           Based on Reinforcement Learning

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      Authors: Qingyu Qu;Kexin Liu;Wei Wang;Jinhu Lü;
      Pages: 5823 - 5834
      Abstract: The rapid development of the aerospace industry puts forward the urgent need for the evolution of autonomous spacecraft rendezvous technology, which has gained significant attention recently due to increased applications in various space missions. This article studies the relative position tracking problem of the autonomous spacecraft rendezvous under the requirement of collision avoidance. An exploration-adaptive deep deterministic policy gradient (DDPG) algorithm is proposed to train a definite control strategy for this mission. Similar to the DDPG algorithm, four neural networks are used in this method, where two of them are used to generate the deterministic policy, whereas the other two are used to score the obtained policy. Differently, adaptive noise is introduced to reduce the possibility of oscillations and divergences and to cut down the unnecessary computation by weakening the exploration of stabilization problems. In addition, in order to effectively and quickly adapt to some other similar scenarios, a metalearning-based idea is introduced by fine-tuning the prior strategy. Finally, two numerical simulations show that the trained control strategy can effectively avoid the oscillation phenomenon caused by the artificial potential function. Benefiting from this, the trained control strategy based on deep reinforcement learning technology can decrease the energy consumption by 16.44% during the close proximity phase, compared with the traditional artificial potential function method. Besides, after introducing the metalearning-based idea, a strategy available for some other perturbed scenarios can be trained in a relatively short period of time, which illustrates its adaptability.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Safety Control Design With Flight Envelope Protection and Reference
           Command Generation

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      Authors: Xiang Yu;Xiaobin Zhou;Youmin Zhang;Lei Guo;Sijun Ye;Xiaoyan Peng;
      Pages: 5835 - 5848
      Abstract: When confronting different levels of aircraft actuator faults, a safety control system without considering the postfault performance degradation raises the probability of flight incidents. This article presents a safety control scheme against actuator malfunctions, on the basis of flight envelope protection and reference command regeneration. First, with respect to a high-fidelity aircraft model, a flight envelope estimation method is developed to respect to the state limits of postfault aircraft (i.e., trim and maneuvering envelopes). Simultaneously, in an attempt to alleviate the burden of the damaged actuators, a degraded reference command is online regenerated, thus, maintaining the closed-loop aircraft within the safe flight envelope. Second, a sliding mode control scheme with the integration of a prescribed performance function is proposed to track the degraded reference command. When comparing to the existing control schemes, the gap between safety control and reference command regeneration is bridged to ameliorate practical flight safety. Finally, comparative simulation studies, based on the high-fidelity Boeing 747 longitudinal model, are conducted to examine the effectiveness of the proposed scheme.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Hybrid Ground-Space Target Visit Problem With a Coplanar Impulse

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      Authors: Haiyang Zhang;Gang Zhang;Longfei Tian;
      Pages: 5849 - 5859
      Abstract: The hybrid ground–space target visit problem is solved by applying a single coplanar impulse. Considering the linear $J_{2}$ perturbation, the ground target visit problem is transformed into the ground track adjustment problem, and it is expressed as a time constraint equation. The space target visit problem is viewed as the orbit interception (or flyby) problem, and it is expressed as time and radius constraint equations. Then, based on Gauss’s variational equations, the hybrid ground–space target visit problem is simplified into solving a single-variable equation only of maneuver position. Finally, the exact solution under the high-precision dynamic model is obtained by the differential correction method. Several numerical examples are presented to validate the efficiency of this approach, and the results show that there are a series of discrete solutions to this problem.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Mode Recognition of Multifunction Radars for Few-Shot Learning Based on
           Compound Alignments

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      Authors: Zilin Zhang;Yan Li;Qihang Zhai;Yunjie Li;Meiguo Gao;
      Pages: 5860 - 5874
      Abstract: The multifunctional radars can switch among a variety of fine-grained working modes, which often have flexible modulation types and programmable parameters. In an electromagnetic reconnaissance system, the process of identifying different working modes in pulse sequences guarantees the subsequent intention analysis and assists in devising a jamming strategy. Most of the existing working mode recognition methods attempt to establish a machine learning mechanism by training a model using a large number of annotated samples. However, this is hardly applicable in the real-world scenarios where only a few samples can be intercepted in advance. As the labeled signal samples are expensive, one direction is to augment the dataset by generating either samples or signal features in an embedding space. In this article, inspired by the fact that different modalities of the same working modes are generated from a set of fixed parameters, a few-shot learning framework based on compound alignments is proposed. Three branches, which take observations of long windows, observations of short windows, and coded semantic attributes as inputs, are aligned in both the latent variable space and the reconstruction space to learn a shared embedded space. A simple soft-max classifier is trained by sampling sufficient instances in the learned space to realize the final identification process. The experimental results and analysis show that the proposed method achieves excellent performance for fine-grained working mode recognition even for a small number of observed pulses. In addition, the proposed method is robust under different nonideal conditions, such as noise contamination, incomplete sequences, and spurious pulses.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Analytical Nonlinear Observability Analysis for Spacecraft Autonomous
           Relative Navigation

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      Authors: Tianshu Dong;Dayi Wang;Fangzhou Fu;Maodeng Li;Bowen Hou;Chao Xu;
      Pages: 5875 - 5893
      Abstract: Spacecraft autonomous relative navigation is widely required in the field of on-orbit services. In previous works, it has been shown that the monocular sequential images (MSIs) is a cost-effective autonomous relative navigation method. However, given the lack of size and initial orbit of noncooperative targets, the relative navigation system (RNS) with MSIs cannot ensure to estimate the entire relative motion states during the whole observation process. It is critical to perform an online observability analysis for RNS with MSIs to ensure autonomous relative motion estimation validity and accuracy. An analytical nonlinear observability analysis method is proposed in this article to improve the computational efficiency. We study the attributes of the relative orbit dynamics model and Lie algebra operations, and find that the observability matrix is only related to the limited states. The relationship among those states, rank, and the Frobenius norm of observability matrix is demonstrated, which simplify the observability criteria conditions and the degree measurements in analytical forms. In addition, based on the proposed method, we analyze the influence of the orbital manifolds on the observability degree of RNS with MSIs. Several suggestions are proposed for orbital manifolds detecting. The conclusion is a theoretical supplement to the past numerical research results. Finally, we implement the numerical simulations, and the current study’s findings verified the effectiveness of the proposed observability analysis method.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • An Adaptive Topology Optimization Strategy for Intersatellite Links in
           GNSS

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      Authors: Kai Han;Bingbing Xu;Fengwei Shao;Wenbin Gong;Qianyi Ren;Jiachao Chang;
      Pages: 5894 - 5907
      Abstract: Intersatellite links (ISLs) are an effective way for the global navigation satellite system (GNSS) to reduce its dependence on ground infrastructures, which guarantees constellation orbit determination and satellite communication. When the number of onboard Ka-band antennas is less than that of visible satellites, ISL assignment of GNSS cause a problem. For the problem of ISL scheduling, considering that the result of the allocated link has a feedback effect on the subsequent link assignment as a priori knowledge, an adaptive topology optimization algorithm based on signed variance is proposed. In order to meet the requirements of the communication and ranging performance, the time slots are divided into communication time slots and ranging time slots. Taking the communication time delay from overseas satellites to anchor satellites and position dilution of precision as measurement indexes, the proposed strategy is simulated for 10 080 min. The results show that the ranging performance of this strategy is better than other recently published methods, which verifies the effectiveness of signed variance for adaptive link planning and is also beneficial to the survivability of constellation.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Fusion of Labeled RFS Densities With Different Fields of View

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      Authors: Lin Gao;Giorgio Battistelli;Luigi Chisci;
      Pages: 5908 - 5924
      Abstract: In this article, the interest is on the fusion of labeled random finite set (LRFS) densities computed by sensors having different fields of view (FoVs). In order to deal with different FoVs of the local densities, the global label space is divided into disjoint subspaces, which represent the exclusive FoVs and the common FoV of the agents, and a method based on the minimization of the Kullback–Leilber divergence from the resulting factorized density to the original one is proposed to decompose each local density into the subdensities defined in the corresponding subspaces. Then, fusion is performed according to a constrained minimum information loss criterion: the local subdensities are fused, subspace by subspace, into global ones, and the global density is obtained by multiplying the global subdensities. The application of the proposed approach to two important types of LRFS density, i.e., marginalized $delta$-generalized labeled multi-Bernoulli and labeled multi-Bernoulli densities, is developed in this article. Furthermore, in order to tackle the label mismatching issue arising in practical applications, an assignment optimization of a suitably defined cost is carried out so as to match labels from different agents. Finally, the performance of the proposed fusion approach is assessed via simulation experiments.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Enhancement of a State-of-the-Art RL-Based Detection Algorithm for Massive
           MIMO Radars

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      Authors: Francesco Lisi;Stefano Fortunati;Maria Sabrina Greco;Fulvio Gini;
      Pages: 5925 - 5931
      Abstract: In the present article, a reinforcement learning (RL)-based adaptive algorithm to optimize the transmit beampattern for a colocated massive multiple-input multiple-output (MIMO) radar is presented. Under the massive MIMO regime, a robust Wald-type detector, able to guarantee certain detection performances under a wide range of practical disturbance models, has been recently proposed. Furthermore, an RL/cognitive methodology has been exploited to improve the detection performance by learning and interacting with the surrounding unknown environment. Building upon previous findings, we develop here a fully adaptive and data-driven scheme for the selection of the hyperparameters involved in the RL algorithm. Such an adaptive selection makes the Wald-RL-based detector independent of any ad hoc, and potentially suboptimal, manual tuning of the hyperparameters. Simulation results show the effectiveness of the proposed scheme in harsh scenarios with strong clutter and low SNR values.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • The “Co-centered Orthogonal Loop/Dipole” (COLD) Array’s “Spatial
           Matched Filter” Beam-Steering

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      Authors: Yi Zhang;Kainam Thomas Wong;
      Pages: 5932 - 5936
      Abstract: Consider an electrical dipole and a magnetic loop—co-centered and axially aligned. This compact pair of line antennas is centro-symmetric and polarization-sensitive but suffers no mutual coupling. Their “spatial matched filter” beam-steering bias will be analytically derived here for the first time in the open literature. The key findings include the following: 1) The elevation-azimuthal directional beam-pattern is multiplicatively separable from the polarizational beam-pattern. 2) No pointing bias exists in polarization (i.e., the nominal “look polarization” always gives the actual peak polarization). 3) No polarizational sidelobe exists. 4) The actual peak polar direction is always normal to the “co-centered orthogonal loop and dipole” array axis regardless of the nominal polar “look direction.”
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Multilook Polarimetric 3-D Interferometric ISAR Imaging

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      Authors: James Park;Raghu G. Raj;Marco Martorella;Elisa Giusti;
      Pages: 5937 - 5943
      Abstract: This article introduces polarimetric 3-D interferometric inverse synthetic aperture radar (ISAR) imaging process using multiple phase centers via a spatio-sensor multilook algorithm. This approach enables one to take effective advantage of polarimetric scattering mechanisms in 3-D target representations, which may improve target classification and identification. In addition, the multilook algorithm enhances the accuracy of height estimation in noisy conditions. The 3-D interferometric ISAR (InISAR) imaging process is validated using the backhoe synthetic data released by the Air Force Research Laboratory.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Efficient ISAR Imaging Approach for Nonuniformly Rotating Targets Based on
           ICPF-PPP

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      Authors: Ki-Bong Kang;Byung-Soo Kang;Sang-Hong Park;Kyung-Tae Kim;
      Pages: 5944 - 5953
      Abstract: In this article, we introduce an improved algorithm for practical inverse synthetic aperture radar (ISAR) imaging, by utilizing the advantages of prominent point processing (PPP) and integrated cubic phase function (ICPF) in an efficient way. The proposed ICPF-PPP algorithm achieves a significant improvement compared to the conventional range-Doppler processing for ISAR imaging of a nonuniformly rotating target in terms of both image quality and computational efficiency. The experimental results using real datasets show that the proposed autofocus chain is highly efficient in forming ISAR images of a nonuniformly rotating target.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Phase-Time Method: Accurate Doppler Measurement for Iridium NEXT Signals

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      Authors: Changyu Huang;Honglei Qin;Chao Zhao;Huaiyuan Liang;
      Pages: 5954 - 5962
      Abstract: Opportunistic positioning utilizing low Earth orbit satellite (LEO) signals mostly adopts Doppler positioning, the performance of which largely depends on the accuracy of Doppler measurement at the receiver. Traditional Doppler measurement methods for Iridium NEXT signals are limited and mostly implemented in frequency domain, which cannot avoid the limitation of fast Fourier transform operation. Aiming at this problem, this article proposes a method achieving accurate Doppler measurement of Iridium NEXT signal in time domain, namely phase-time method. This method achieves accurate Doppler measurement by measuring the change rate of the signal phase over time. Experiments were implemented using real Iridium NEXT signals, and the results have demonstrated that compared with the existing method, the Doppler measurement value obtained by the phase-time method possesses higher accuracy. Taking the Doppler measurement values obtained by the phase-time method as observations, the stability and reliability of Doppler positioning can be significantly improved. The proposed phase-time method is of great significance to LEO-signal frequency estimation in time domain, and further contributes to opportunistic positioning using LEO constellations.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Hand Gesture Recognition Using Radial and Transversal Dual Micromotion
           Features

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      Authors: Xiangrong Wang;Weiliang Li;Victor C. Chen;
      Pages: 5963 - 5973
      Abstract: Most of the work in hand gesture recognition (HGR) focuses on developing diverse classification algorithms based on micro-Doppler (mD) spectrogram, that is 1-D motion along the radial direction. In this work, we exert effort on the radar system and preprocessing methods to extract 2-D motions for HGR. Specifically, we utilize an interferometric radar with two widely spaced receivers to obtain both radial and transversal micromotion features of hand gestures. In the preprocessing stage, as pre-interferometry is nonlinear multiplication in time domain, both the increased noise level and unwanted cross-terms may reduce its usefulness for HGR. To solve these problems, we propose a post-interferometric preprocessing method in frequency domain, which is capable of reducing noise level of the obtained spectrogram and suppressing the nuisance cross-terms. We measure four pairs of symmetric hand gestures from three persons and compare the HGR accuracy using different preprocessing methods. Experimental results show that the mD processing combined with post-interferometry give the highest HGR accuracy of over 99%.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • Nonlinear Gaussian Filtering With Network-Induced Delay in Measurements

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      Authors: Guddu Kumar;Sumanta Kumar Nanda;Alok Kumar Verma;Vimal Bhatia;Abhinoy Kumar Singh;
      Pages: 5974 - 5981
      Abstract: This article designs an advanced Gaussian filtering algorithm for improving accuracy in the presence of time-delay in measurements. The proposed method uses a Bernoulli random variable and a geometric random variable to reformulate the delay modeling strategy. Subsequently, the traditional Gaussian filtering method for the modified measurement model is rederived. The proposed method precludes two major drawbacks of the existing delay filtering methods, including a priori knowledge of many delay probabilities and an ambiguous selection of an upper bound of delay. Thus, the proposed method outperforms the existing delay filtering methods and the same is validated from the simulation results. The proposed method is a general modification of the traditional Gaussian filtering and applies to all conventionally popular Gaussian filters.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
  • On the Performance of UAV Relaying With Reconfigurable Antenna and Media
           Based Modulation in the Presence of Shadowed Fading

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      Authors: Ayse Betul Buyuksar;Eylem Erdogan;Ibrahim Altunbas;
      Pages: 5982 - 5988
      Abstract: Unmanned aerial vehicles (UAVs) have attracted significant interest from the academia and industry most recently. Motivated by the wide usage of UAVs, this letter considers UAV communication with reconfigurable antenna (RA) in the presence of fading and shadowing effects, which occur due to tall buildings and skyscrapers in urban areas. More precisely, RA offers to receive information through mirror activation patterns (MAPs) so that it can achieve a receive diversity with decreased error probability by using only one radio frequency chain. Also, media-based modulation (MBM) technique with MAPs can be exploited by using RAs with reduced cost. To quantify the performance of the proposed UAV system, we derive a tight upper bound for the overall error probability by considering approximated channel model based on the standardization studies. The results have shown that RAs can make the overall system more resilient to shadowing and fading effects in terms of error performance, and they are energy efficient.
      PubDate: Dec. 2022
      Issue No: Vol. 58, No. 6 (2022)
       
 
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