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  Subjects -> ELECTRONICS (Total: 207 journals)
Showing 1 - 200 of 277 Journals sorted by number of followers
IEEE Transactions on Aerospace and Electronic Systems     Hybrid Journal   (Followers: 335)
Control Systems     Hybrid Journal   (Followers: 289)
IEEE Transactions on Geoscience and Remote Sensing     Hybrid Journal   (Followers: 235)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 213)
Electronics     Open Access   (Followers: 177)
Advances in Electronics     Open Access   (Followers: 171)
Electronics For You     Partially Free   (Followers: 159)
Electronic Design     Partially Free   (Followers: 156)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 145)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 94)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 92)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 88)
IEEE Transactions on Industrial Electronics     Hybrid Journal   (Followers: 88)
IEEE Transactions on Software Engineering     Hybrid Journal   (Followers: 87)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 81)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 70)
IET Power Electronics     Open Access   (Followers: 69)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 66)
IEEE Embedded Systems Letters     Hybrid Journal   (Followers: 59)
IEEE Transactions on Industry Applications     Hybrid Journal   (Followers: 58)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 57)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 55)
Advances in Power Electronics     Open Access   (Followers: 50)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 46)
IEEE Nanotechnology Magazine     Hybrid Journal   (Followers: 44)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 42)
IEEE Transactions on Biomedical Engineering     Hybrid Journal   (Followers: 36)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 34)
IET Microwaves, Antennas & Propagation     Open Access   (Followers: 33)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 32)
Electronics Letters     Open Access   (Followers: 32)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 32)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 30)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 28)
Microelectronics and Solid State Electronics     Open Access   (Followers: 27)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 25)
Journal of Sensors     Open Access   (Followers: 23)
International Journal of Power Electronics     Hybrid Journal   (Followers: 23)
IEEE Reviews in Biomedical Engineering     Hybrid Journal   (Followers: 21)
IEEE/OSA Journal of Optical Communications and Networking     Hybrid Journal   (Followers: 20)
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 19)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 18)
IET Wireless Sensor Systems     Open Access   (Followers: 18)
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 17)
Journal of Artificial Intelligence     Open Access   (Followers: 16)
Circuits and Systems     Open Access   (Followers: 16)
Archives of Electrical Engineering     Open Access   (Followers: 16)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 16)
International Journal of Control     Hybrid Journal   (Followers: 14)
Advances in Microelectronic Engineering     Open Access   (Followers: 14)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 14)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 13)
IETE Journal of Research     Open Access   (Followers: 13)
IEEE Solid-State Circuits Magazine     Hybrid Journal   (Followers: 13)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 13)
Nature Electronics     Hybrid Journal   (Followers: 13)
Machine Learning with Applications     Full-text available via subscription   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 12)
IEEE Transactions on Broadcasting     Hybrid Journal   (Followers: 12)
IEEE Transactions on Learning Technologies     Full-text available via subscription   (Followers: 12)
IEEE Women in Engineering Magazine     Hybrid Journal   (Followers: 12)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 12)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 12)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 11)
Frontiers in Electronics     Open Access   (Followers: 11)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 10)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Batteries     Open Access   (Followers: 10)
Superconductivity     Full-text available via subscription   (Followers: 10)
IETE Technical Review     Open Access   (Followers: 9)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 9)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 9)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 9)
ACS Applied Electronic Materials     Open Access   (Followers: 9)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 8)
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 8)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 8)
Journal of Signal and Information Processing     Open Access   (Followers: 8)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 8)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 8)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 8)
International Journal of Antennas and Propagation     Open Access   (Followers: 7)
Annals of Telecommunications     Hybrid Journal   (Followers: 7)
IEEE Transactions on Autonomous Mental Development     Hybrid Journal   (Followers: 7)
IEEE Magnetics Letters     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
Open Journal of Antennas and Propagation     Open Access   (Followers: 7)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 7)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 7)
Energy Storage Materials     Full-text available via subscription   (Followers: 7)
Chinese Journal of Electronics     Open Access   (Followers: 7)
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access   (Followers: 7)
Electronic Markets     Hybrid Journal   (Followers: 6)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 6)
International Journal of Electronics     Hybrid Journal   (Followers: 6)
Journal of Electronics (China)     Hybrid Journal   (Followers: 6)
Journal of Field Robotics     Hybrid Journal   (Followers: 6)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 6)
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 6)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal   (Followers: 6)
EPE Journal : European Power Electronics and Drives     Hybrid Journal   (Followers: 6)
Advanced Materials Technologies     Hybrid Journal   (Followers: 6)
Nanotechnology, Science and Applications     Open Access   (Followers: 5)
IEEE Transactions on Services Computing     Hybrid Journal   (Followers: 5)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 5)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 5)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 5)
Metrology and Measurement Systems     Open Access   (Followers: 5)
IEEE Pulse     Hybrid Journal   (Followers: 5)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 5)
Journal of Optoelectronics Engineering     Open Access   (Followers: 5)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
Journal of Power Electronics     Hybrid Journal   (Followers: 5)
Sensors International     Open Access   (Followers: 5)
e-Prime : Advances in Electrical Engineering, Electronics and Energy     Open Access   (Followers: 5)
Materials Today Electronics     Open Access   (Followers: 5)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 4)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 4)
IEEE Transactions on Haptics     Hybrid Journal   (Followers: 4)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 4)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
IETE Journal of Education     Open Access   (Followers: 4)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 4)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
EPJ Quantum Technology     Open Access   (Followers: 4)
Wireless and Mobile Technologies     Open Access   (Followers: 4)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Informatik-Spektrum     Hybrid Journal   (Followers: 3)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 3)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 3)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal   (Followers: 3)
Power Electronic Devices and Components     Open Access   (Followers: 3)
Journal of Nuclear Cardiology     Hybrid Journal   (Followers: 2)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Journal of Semiconductors     Full-text available via subscription   (Followers: 2)
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 2)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 2)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 2)
Journal of Information and Telecommunication     Open Access   (Followers: 2)
Advancing Microelectronics     Hybrid Journal   (Followers: 2)
IET Smart Grid     Open Access   (Followers: 2)
IET Energy Systems Integration     Open Access   (Followers: 2)
Transactions on Electrical and Electronic Materials     Hybrid Journal   (Followers: 2)
Energy Storage     Hybrid Journal   (Followers: 2)
IEEE Open Journal of the Industrial Electronics Society     Open Access   (Followers: 2)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
Journal of Electrical Bioimpedance     Open Access   (Followers: 1)
International Journal of High Speed Electronics and Systems     Hybrid Journal   (Followers: 1)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Електротехніка і Електромеханіка     Open Access   (Followers: 1)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 1)
Edu Elektrika Journal     Open Access   (Followers: 1)
Technical Report Electronics and Computer Engineering     Open Access   (Followers: 1)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 1)
Open Electrical & Electronic Engineering Journal     Open Access   (Followers: 1)
International Journal of Hybrid Intelligence     Hybrid Journal   (Followers: 1)
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 1)
Solid State Electronics Letters     Open Access   (Followers: 1)
Ural Radio Engineering Journal     Open Access   (Followers: 1)
IEEE Open Journal of Circuits and Systems     Open Access   (Followers: 1)
IEEE Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 1)
Elektronika ir Elektortechnika     Open Access   (Followers: 1)
Automatika : Journal for Control, Measurement, Electronics, Computing and Communications     Open Access   (Followers: 1)
npj Flexible Electronics     Open Access  
Transactions on Cryptographic Hardware and Embedded Systems     Open Access  
Emitor : Jurnal Teknik Elektro     Open Access  
IEEE Solid-State Circuits Letters     Hybrid Journal  
IEEE Open Journal of Industry Applications     Open Access  
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal  
Journal of Electronic Science and Technology     Open Access  
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Journal of Engineered Fibers and Fabrics     Open Access  
Jurnal Teknologi Elektro     Open Access  
IET Nanodielectrics     Open Access  
Elkha : Jurnal Teknik Elektro     Open Access  
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Jurnal Teknik Elektro     Open Access  
IACR Transactions on Symmetric Cryptology     Open Access  
Acta Electronica Malaysia     Open Access  
Bioelectronics in Medicine     Hybrid Journal  
Problemy Peredachi Informatsii     Full-text available via subscription  
Jurnal Rekayasa Elektrika     Open Access  
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Visión Electrónica : algo más que un estado sólido     Open Access  
Telematique     Open Access  
International Journal of Nanoscience     Hybrid Journal  
Semiconductors and Semimetals     Full-text available via subscription  

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Similar Journals
Journal Cover
IEEE Transactions on Aerospace and Electronic Systems
Journal Prestige (SJR): 0.611
Citation Impact (citeScore): 3
Number of Followers: 335  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0018-9251
Published by IEEE Homepage  [228 journals]
  • IEEE Aerospace and Electronic Systems Society Information

    • Free pre-print version: Loading...

      Pages: C2 - C2
      Abstract: null
      PubDate: THU, 08 FEB 2024 09:18:34 -04
      Issue No: Vol. 60, No. 1 (2024)
       
  • Guest Editorial for the TAES Special Section on Deep Learning for Radar
           Applications

    • Free pre-print version: Loading...

      Authors: Sevgi Z. Gurbuz;Igal Bilik;Luke Rosenberg;
      Pages: 4 - 7
      Abstract: It has been roughly a decade since the first papers using deep neural networks (DNNs) for radar applications were published. Deep learning has revolutionized almost every technical area, from computer vision and natural language processing to health, finance, and biology—any field where data can be analyzed to provide insight. However, in radar applications, deep learning faces unique challenges due to the phenomenology of radio frequency (RF) propagation that creates essential differences in the data itself and impacts the design of DNNs for radar signal analysis [1], [2], [3].
      PubDate: THU, 08 FEB 2024 09:18:33 -04
      Issue No: Vol. 60, No. 1 (2024)
       
  • Deep-Learning-Based Multiband Signal Fusion for 3-D SAR Superresolution

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      Authors: Josiah W. Smith;Murat Torlak;
      Pages: 8 - 24
      Abstract: 3-D synthetic aperture radar (SAR) is widely used in many security and industrial applications requiring high-resolution imaging of concealed or occluded objects. The ability to resolve intricate 3-D targets is essential to the performance of such applications and depends directly on the system bandwidth. However, because high-bandwidth systems face several prohibitive hurdles, an alternative solution is to operate multiple radars at distinct frequency bands and fuse the multiband signals. Current multiband signal fusion methods assume a simple target model and a small number of point reflectors, which is invalid for realistic security screening and industrial imaging scenarios wherein the target model effectively consists of a large number of reflectors. To the best of our knowledge, this study presents the first use of deep learning for multiband signal fusion. The proposed network, called $kR$-Net, employs a hybrid, dual-domain complex-valued convolutional neural network to fuse multiband signals and impute the missing samples in the frequency gaps between subbands. By exploiting the relationships in both the wavenumber domain and wavenumber spectral domain, the proposed framework overcomes the drawbacks of existing multiband imaging techniques for realistic scenarios at a fraction of the computation time of existing multiband fusion algorithms. Our method achieves high-resolution imaging of intricate targets previously impossible using conventional techniques and enables finer resolution capacity for concealed weapon detection and occluded object classification using multiband signaling without requiring more advanced hardware. Furthermore, a fully integrated multiband imaging system is developed using commercially available millimeter-wave (mmWave) radars for efficient multiband imaging. Using two mmWave radars, each with a bandwidth of 4 GHz operating at 60 and 77 GHz, the proposed $kR$-Net is employed to achieve an effective bandwidth of 21 GHz by robustly estimating the full-band signal. Additionally, the generalizability of the proposed technique is demonstrated across multiband sensing scenarios in the mmWave and terahertz frequencies. Extensive numerical simulations and empirical experiments are conducted to illustrate the superiority of our approach over existing methods for a diverse set of realistic 3-D SAR imaging scenarios.
      PubDate: TUE, 25 APR 2023 10:02:34 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Classification of ISAR Ship Imagery Using Transfer Learning

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      Authors: Luke Rosenberg;Weiliang Zhao;Anthony Heng;Len Hamey;Si Tran Nguyen;Mehmet A. Orgun;
      Pages: 25 - 36
      Abstract: Inverse synthetic aperture radar (ISAR) is a common radar imaging technique used to characterize and classify non-cooperative targets. Traditional classification approaches use geometric features extracted from the images of known targets to form a training dataset that is later used to classify an unknown target. While these approaches work reasonably well, deep learning-based techniques have demonstrated significant improvements over conventional processing schemes in many areas of radar. However, the application of ISAR image classification is difficult when there are only small training datasets available. In this article, we address the small dataset problem by utilizing transfer learning. Different approaches are considered that can take advantage of the ship aspect angle to improve the overall stability and improve the final classification result. The new classification results are then compared with a traditional classification approach and a simple three-layer convolutional neural network. In addition, to better understand how the neural networks are working, saliency maps are used to visualize the trained network.
      PubDate: MON, 24 JUL 2023 10:02:43 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Transfer-Based DRL for Task Scheduling in Dynamic Environments for
           Cognitive Radar

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      Authors: Sunila Akbar;Raviraj S. Adve;Zhen Ding;Peter W. Moo;
      Pages: 37 - 50
      Abstract: Cognitive radars sense, interact with, and learn from the environment continuously. This paradigm can be applied to a multifunction radar (MFR), which performs multiple functions, such as surveillance, tracking, and communications amongst others. To execute these tasks, a radar resource management (RRM) module assigns the available resources to these functions while accounting for tasks' parameters, including their priority. This article focuses on the problem of task scheduling within a time window. For the time resource, RRM becomes especially challenging as 1) task requirements can be extremely heterogeneous with multiple priority categories and 2) the scheduling policy should be adaptable to a dynamic environment. Adapting to a nonstationary environment is a key benefit of cognitive radar. While previous works have developed effective techniques for homogeneous tasks in static environments, in this article, we make two key contributions: we formulate a fairly general model for the distributions of task parameters including, specifically, task priorities and delay tolerances; second, we develop the use of transfer learning (TL) within a deep reinforcement learning (DRL) framework to address the challenge of adaptability to a varying environment. Our approach builds on using a Monte Carlo Tree Search (MCTS) aided by a deep neural network (DNN). We show that TL allows accelerated training by transferring the policy learned by training the DNN-based MCTS on an initial parameter distribution (environment) to the policy required for a new distribution. We show that our TL-based approach provides adaptability to both rapid or gradual changes of environment. Our results illustrate the robustness and the computation gains achieved. Moreover, our results show that a multinetwork approach is to be preferred over single network being trained on a series of differing environments.
      PubDate: FRI, 22 SEP 2023 09:18:16 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Small Target Detection in a Radar Surveillance System Using Contractive
           Autoencoders

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      Authors: Simon Wagner;Winfried Johannes;Denisa Qosja;Stefan Brüggenwirth;
      Pages: 51 - 67
      Abstract: With the rapid development of unpiloted aerial vehicles (UAVs), also known as drones, in recent years, the need for surveillance systems that are able to detect drones has grown as well. Radar is the technology with the potential to fulfill this task, and several previous publications show examples of radar detection and classification schemes. The purpose of this article is related to the detection scheme used in these approaches. Most surveillance systems use a background subtraction and a threshold to detect targets. This threshold often depends on a model of the radar noise and the background, which is imperfect by nature. The approach presented here uses a data-driven machine learning algorithm that is trained with measured background profiles of the radar and is applied afterward to the given background for target detection. This scheme can in general be applied to any detection problem in a fixed area, but is shown here with examples from measurements of drones and persons. The results show that the chosen approach gives better detection rates for low false alarm rates with real data than that given by background subtraction.
      PubDate: TUE, 07 MAR 2023 10:13:24 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Machine Learning for UAV Classification Employing Mechanical Control
           Information

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      Authors: Ahmed N. Sayed;Omar M. Ramahi;George Shaker;
      Pages: 68 - 81
      Abstract: Range–Doppler images are widely used to classify different types of unmanned air vehicles (UAVs) because each UAV has a unique range–Doppler signature. However, a UAV's range–Doppler signature depends on its movement mechanism. This is why a classifier's accuracy would be degraded if the effect of the mechanical control system of UAVs was not taken into consideration, which may lead to a nonunique signature of a UAV while in-flight. In this article, a full-wave electromagnetic CAD tool is used to investigate the effect of the control systems of two quadcopters, a hexacopter, and a helicopter UAVs on their range–Doppler signatures. A mechanical control-based machine learning (ML) algorithm is introduced to classify the four UAVs. Different ML algorithms were applied to the generated datasets that considered the mechanical control information of UAVs. The convolutional neural network algorithms provided robust performance reaching an accuracy of higher than 90%.
      PubDate: TUE, 02 MAY 2023 10:14:46 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Differentiable Rendering for Synthetic Aperture Radar Imagery

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      Authors: Michael C. Wilmanski;Jonathan I. Tamir;
      Pages: 82 - 93
      Abstract: There is rising interest in differentiable rendering, which allows explicitly modeling geometric priors and constraints in optimization pipelines using first-order methods such as backpropagation. Incorporating such domain knowledge can lead to deep neural networks that are trained more robustly and with limited data, as well as the capability to solve ill-posed inverse problems. Existing efforts in differentiable rendering have focused on imagery from electro-optical sensors, particularly conventional RGB-imagery. In this article, we propose an approach for differentiable rendering of synthetic aperture radar (SAR) imagery, which combines methods from 3-D computer graphics with neural rendering. We demonstrate the approach on the inverse graphics problem of 3-D object reconstruction from limited SAR imagery using high-fidelity simulated SAR data.
      PubDate: THU, 10 AUG 2023 10:01:17 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Target Classification for 3D-ISAR Using CNNs

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      Authors: Chow Yii Pui;Brian Ng;Luke Rosenberg;Tri-Tan Cao;
      Pages: 94 - 105
      Abstract: In maritime surveillance, inverse synthetic aperture radar (ISAR) is a technique for imaging non-cooperative targets, with classification typically performed by the radar operator. By automating the target classification process, the operator workload will be reduced significantly and the classification accuracy can be improved. Traditional classification approaches use geometric features extracted from images of known targets to form a training dataset that is later used to classify an unknown target. While these approaches work reasonably well, deep learning based techniques have recently demonstrated significant improvements over conventional processing schemes in many areas of radar. The classification of traditional 2D-ISAR imagery is difficult due to the motion of the sea causing a wide range of imagery. The 3D-ISAR technique was developed as an alternative representation with the target represented by a three-dimensional point cloud. In this article, we investigate how 3D-ISAR can be used for the classification of maritime targets. The proposed scheme makes use of features extracted from the 3D-ISAR generated point cloud of the target from different perspectives (i.e. side, top and front views) to form three point density images. These are then fed into a convolutional neural network to classify the targets.
      PubDate: THU, 27 APR 2023 10:10:26 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Neural-Network-Based DOA Estimation in the Presence of Non-Gaussian
           Interference

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      Authors: Stefan Feintuch;Joseph Tabrikian;Igal Bilik;Haim Permuter;
      Pages: 119 - 132
      Abstract: This work addresses the problem of direction-of-arrival (DOA) estimation in the presence of non-Gaussian, heavy-tailed, and spatially-colored interference. Conventionally, the interference is considered to be Gaussian-distributed and spatially white. However, in practice, this assumption is not guaranteed, which results in degraded DOA estimation performance. Maximum likelihood DOA estimation in the presence of non-Gaussian and spatially-colored interference is computationally complex and not practical. Therefore, this work proposes a neural network (NN)-based DOA estimation approach for spatial spectrum estimation in multisource scenarios with an a priori unknown number of sources in the presence of non-Gaussian spatially-colored interference. The proposed approach utilizes a single NN instance for simultaneous source enumeration and DOA estimation. It is shown via simulations that the proposed approach significantly outperforms conventional and NN-based approaches in terms of probability of resolution, estimation accuracy, and source enumeration accuracy in conditions of low signal-to-interference ratio, small-sample support, and when the angular separation between the source DOAs and the spatially-colored interference is small.
      PubDate: TUE, 18 APR 2023 10:02:30 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Sparse Array Design for Optimum Beamforming Using Deep Learning

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      Authors: Syed A. Hamza;Moeness G. Amin;
      Pages: 133 - 144
      Abstract: This article considers sparse array design for receive beamforming achieving maximum signal-to-interference plus noise ratio (MaxSINR). We develop a design approach based on supervised neural network where class labels are generated using an efficient sparse beamformer spectral analysis (SBSA) approach. SBSA uses explicit information of the unknown narrowband interference environment for training the network and bears close performance to training using exhaustive search by enumerations which is computationally prohibitive for large arrays. The employed deep neural network (DNN) effectively approximates the unknown mapping from the input received data spatial correlations to the output of sparse configuration with effective interference mitigation capability. The problem is posed as a classification problem where the sparse array configuration achieving MaxSINR is one-hot encoded, and indicated by the output layer of DNN. We evaluate the performance of the DNN in terms of the sparse array classification accuracy as well as in terms of the ability of the classified sparse array to mitigate interference and maximize signal power. It is shown that the DNN effectively learns the optimal sparse configuration which has desirable SINR characteristics, hence paving the way for efficient real-time implementation.
      PubDate: TUE, 13 JUN 2023 10:01:08 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Interferometric Passive Radar Imaging With Deep Denoising Priors

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      Authors: Samia Kazemi;Bariscan Yonel;Birsen Yazici;
      Pages: 145 - 156
      Abstract: Passive radar has advantages over its active counterpart in terms of cost and stealth. In this article, we address the passive radar imaging problem by interferometric inversion using a spectral estimation method with a priori information within a deep learning framework. Cross-correlating the received signals from different look directions mitigates the influence of shared transmitter related phase components despite lack of a cooperative transmitter, and permits tractable inference via interferometric inversion. To this end, we leverage deep architectures for modeling a priori information and for improving sample efficiency of state-of-the-art interferometric inversion methods. Our approach is comprised of an iterative algorithm based on generalizing the power method, and applies denoisers using plug-and-play and regularization by denoising techniques. We evaluate our approach using simulated data for passive synthetic aperture radar by using convolutional neural networks as denoisers, and compare our results with state-of-the-art. The numerical experiment shows that our method can achieve faster reconstruction and superior image quality in sample starved regimes than the state-of-the-art passive interferometric imaging algorithms.
      PubDate: TUE, 28 MAR 2023 10:02:38 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler
           Signatures

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      Authors: Chong Tang;Wenda Li;Shelly Vishwakarma;Fangzhan Shi;Simon Julier;Kevin Chetty;
      Pages: 157 - 167
      Abstract: Motion tracking systems based on optical sensors typically suffer from poor lighting, occlusion, limited coverage, and may raise privacy concerns. Recently, radio-frequency (RF) based approaches using WiFi have emerged which offer low-cost ubiquitous sensing whilst preserving privacy. However, output range-Doppler or time-frequency spectrograms cannot represent human motion intuitively and usually requires further processing. In this study, we propose MDPose, a novel framework for human skeletal motion reconstruction based on WiFi micro-Doppler. MDPose provides an effective solution to represent human activity by reconstructing skeleton models with 17 key points, which can assist with the interpretation of conventional RF sensing outputs in a more understandable way. Specifically, MDPose is implemented over three sequential stages to address various challenges: First, a denoising algorithm is employed to remove any unwanted noise that may affect feature extraction and enhance weak Doppler measurements. Second, a convolutional neural network (CNN)-recurrent neural network (RNN) architecture is applied to learn temporal-spatial dependency from clean micro-Doppler and restore velocity information to key points under the supervision of the motion capture (Mocap) system. Finally, a pose optimisation mechanism based on learning optimisation vectors is employed to estimate the initial skeletal state and to eliminate additional errors. We have conducted comprehensive evaluations in a variety of environments using numerous subjects with a single receiver radar system to demonstrate the performance of MDPose, and report 29.4mm mean absolute error over key points positions on several common daily activities, which has performance comparable to that of state-of-the-art RF-based pose estimation systems.11For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising.
      PubDate: TUE, 14 MAR 2023 10:02:19 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • A Metacognitive Approach to Adaptive Radar Detection

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      Authors: Alexander Stringer;Geoffrey Dolinger;Debra Hogue;Lacey Schley;Justin G. Metcalf;
      Pages: 168 - 185
      Abstract: Detecting objects of interest is one of the core functions of radar systems and doing so in the presence of interference is an ongoing challenge in the domain. Clutter is an especially problematic form of interference that can result in a large number of false alarms. In general, the goal of radar detection systems is to maximize the likelihood of detecting targets while maintaining a constant false alarm rate (CFAR). Adaptive detectors like the generalized likelihood ratio test (GLRT) have been developed to achieve this. However, they are derived assuming that the clutter can be modeled according to a consistent probability distribution. This assumption typically does not hold true in many real-world applications, particularly on airborne or naval systems, which degrades detection performance and eliminates the desired CFAR behavior. In this work, a metacognitive approach to adaptive detection is proposed to achieve CFAR-like behavior over a range of clutter distributions. It is demonstrated that this metacognitive detector maintains CFAR-like behavior when presented with data randomly selected from a range of clutter distribution models (Gaussian, K, and Pareto) and that it matches the performance of the traditional GLRT in Gaussian interference.
      PubDate: MON, 08 MAY 2023 10:14:03 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Incremental Nonlinear Dynamic Inverse-Adaptive Sliding Mode
           Three-Dimensional Angle Constrained Guidance Law Design

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      Authors: Zhenlin Zhang;Ke Zhang;Zhiguo Han;
      Pages: 186 - 201
      Abstract: To address the angle constrained problem of 3-D maneuvering target interception, in this article, a coupled 3-D missile–target relative motion model is proposed, and then, a nonlinear dynamic inverse-adaptive sliding mode control (NDI-ASMC) is designed. Based on NDI-ASMC, an incremental nonlinear dynamic inverse-adaptive sliding mode control (INDI-ASMC) angle constrained guidance law is proposed. With the help of the transformation matrix of the coordinate system, the model under the line-of-sight (LOS) coordinate system is transformed, and then, a missile–target relative motion model is proposed. The model does not contain lead angle terms, reducing the complexity of the model and saving computational amount. An adaptive disturbance suppression term is introduced to tackle the LOS angle constrained problem with the unknown target maneuver, based on this term and the nonlinear dynamic inverse theory, an NDI-ASMC is proposed. The aforementioned disturbance suppression term can adaptively adjust to the target maneuver and does not cause chattering. Considering that the adaptive term of NDI-ASMC increases with the increase of target maneuver, resulting in greater overload, this article designs an INDI-ASMC law that can effectively reduce the overload and increase the final destruction effect on the target. The Lyapunov theory proves the stability of the proposed guidance laws, and the simulation verifies the robustness and feasibility of the guidance laws.
      PubDate: FRI, 15 SEP 2023 10:02:25 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Costate Estimation With Bezier-Curve-Based Shaping Approach for
           Fuel-Optimal Multisatellite Formation Reconfiguration

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      Authors: Lie Yang;Mingying Huo;Wenyu Feng;Ze Yu;Ye Xu;Naiming Qi;
      Pages: 202 - 211
      Abstract: A fast costate estimation technique with shaping-based method is applied to the indirect optimization of fuel-optimal multisatellite formation reconfiguration. The Bezier-curve-based shaping approach is able to solve the approximate optimal trajectory rapidly, which is to estimate the costate by being discretized at Gauss points and substituted into the discrete first-order optimality condition. The performance of costate estimation is verified in three-satellite formation reconfiguration and six-satellite formation configuration initialization. Compared with the direct method's results, the costate estimation can lead to optimal solutions of indirect optimization and only spend 5.8% and 5.9% calculation time of direct method.
      PubDate: FRI, 29 SEP 2023 09:20:26 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Adaptive Radar Detection in Heterogeneous Clutter Plus Thermal Noise via
           the Expectation-Maximization Algorithm

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      Authors: Angelo Coluccia;Alessio Fascista;Danilo Orlando;Giuseppe Ricci;
      Pages: 212 - 225
      Abstract: This article addresses adaptive radar detection of $N$ pulses coherently backscattered by a prospective target in heterogeneous disturbance. As customary $K \geq N$ range cells adjacent to the one under test are used for estimation purposes. The disturbance in each range cell is described by a non-Gaussian model based on a mixture of $L < K$ Gaussian distributions. Gaussian components are characterized by an unknown low-rank matrix plus thermal noise with unknown power level. We first derive a detector inspired by the generalized likelihood ratio test that adaptively estimates the statistical properties of the disturbance from the observed data. To overcome the intractability of the involved maximum-likelihood estimation problem, a suitable approximate strategy based on the expectation-maximization algorithm is developed. This also allows us to classify the cell under test by selecting the “maximum a posteriori Gaussian distribution” for the disturbance (under both hypotheses). Accordingly, a likelihood ratio test is also proposed. An extensive performance analysis, conducted on synthetic data as well as on two different experimental datasets (PhaseOne and IPIX for land and sea radar returns, respectively), shows that the proposed approaches outperform state-of-the-art competitors in terms of both detection capabilities and false alarms control.
      PubDate: MON, 09 OCT 2023 09:18:58 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Detection and Localization of Drones in MIMO CW Radar

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      Authors: Ayhan Yazıcı;Buyurman Baykal;
      Pages: 226 - 238
      Abstract: Low-radar cross section and capability to fly at low speeds make drones challenging targets for radar detection. In the presence of ground moving targets the frequency spectrum is also crowded, which makes the detection of the drones more difficult. Micro-Doppler effect is the main feature used to discriminate drone from other targets and clutter. Typically discrimination is performed after the detection of all the targets. Especially in target dense environments, such as cities, typical approach requires high processing power in order to detect and classify all of the targets. Coverage is also another problem of the typical monostatic radar-based drone detection in cities. Coverage of monostatic radar is easily blocked by buildings. In order to cope with these problems distributed multi-input multi-output (MIMO) continuous wave (CW) radar using MIMO cyclic spectral density (CSD) analysis (MCSD) method is proposed in this article. The MCSD method detects and classifies drones using the cyclic frequency information. In order to make the system simple and low cost, a network of CW radars is used and the localization is performed based on Doppler only localization approach. The simulations and experimental results demonstrate the proof of the concept. Performance and cost analyzes of the MCSD method are also elaborated in this article.
      PubDate: TUE, 03 OCT 2023 09:18:24 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Payload Transporting With Two Quadrotors by Centralized Reinforcement
           Learning Method

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      Authors: Dasheng Lin;Jianda Han;Kun Li;Jianlei Zhang;Chunyan Zhang;
      Pages: 239 - 251
      Abstract: Nowadays, quadrotors find applications in automation and artificial intelligence. Among diverse quadrotor studies, payload transport stands out, posing implementation challenges. Using multiple quadrotors reduces per-quadrotor load while increasing system complexity. Inspired by model-free reinforcement learning, we apply it to position control in a nonlinear two-quadrotor payload system. Our approach employs a reinforcement learning agent guided by the twin delay deep deterministic policy gradient (TD3) algorithm. Its goal is accurate cable-suspended payload delivery and system stabilization. We test the method's robustness by adding noise. Simulation results show that TD3 excels in ideal conditions and handles noise during training and testing, highlighting its effectiveness. This article's scope can be expanded to encompass scenarios involving three or more quadrotors, providing valuable insights for future endeavors.
      PubDate: MON, 02 OCT 2023 09:18:57 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Deep Contrastive Clustering for Signal Deinterleaving

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      Authors: Shuyuan Yang;Xinyi Zhao;Huiling Liu;Chen Yang;Tongqing Peng;Rundong Li;Feng Zhang;
      Pages: 252 - 263
      Abstract: In a complex electromagnetic environment, radar signal deinterleaving (RSD) is a challenging task. In this article, a deep contrastive clustering algorithm (DCCA) is advanced in a new self-supervised paradigm for the accurate RSD without any prior information about radar emitters. First, a contrastive self-supervised deep attention network (CSDAN) is constructed to learn signal representations by using self-defined pseudolabels of augmented signals as supervision. We use CSDAN to learn the differences between different radiation source data and generate deep features suitable for clustering. Three metrics are then used to automatically determine the number of clusters for the subsequent clustering. Extensive experiments are performed on several datasets containing different numbers of emitters. The results show that the proposed DCCA can accurately determine the number of emitters and deinterleave radar pulses. Furthermore, CSDAN can extract discriminative features of emitters with low intraclass similarity and high interclass similarity.
      PubDate: MON, 09 OCT 2023 09:18:58 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Nonlinear Three-Dimensional Guidance for Impact Time and Angle Control
           With Field-of-View Constraint

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      Authors: Pengyu Wang;Chang-Hun Lee;Yuhan Liu;Min-Jea Tahk;
      Pages: 264 - 279
      Abstract: This article presents a nonlinear 3-D guidance law for homing missiles with a field-of-view (FOV) constraint to achieve impact time and angle control against a stationary target. Specifically, a guidance error vector and its nonlinear error dynamics are first developed. The desired impact time and angle can be satisfied as long as the guidance error converges to zero before the interception. The proposed nonlinear error dynamics is then enhanced with an auxiliary function to address the FOV constraint, and the prescribed-time control technique is employed to obtain a feedback guidance law. The guidance framework in this article is rigorously derived under the original 3-D missile–target kinematics model, without decoupling the engagement geometry into two mutually orthogonal planes. Furthermore, different from the existing similar studies, the proposed guidance law does not require explicit time-to-go estimation and numerical iterations, which avoids estimation error and allows for convenient implementation.
      PubDate: WED, 04 OCT 2023 09:19:12 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Cooperative Base Line Interferometer for SWaP Optimized Direction Finding
           Receivers

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      Authors: Sounak Samanta;Mrityunjoy Chakraborty;
      Pages: 280 - 290
      Abstract: Base line interferometer (BLI) is a popular direction of arrival (DOA) estimation technique for electronic warfare applications. For size, weight, and power (SWaP) optimized realization of the BLI, switched mode of operation is preferred which uses fewer number of receiver channels than the number of antenna elements and switches them among the antenna-pairs in a phased manner. Such switched operation, however, results in a suboptimal performance, since, as shown in this article, it reduces the tolerable phase-error margin (TPM), and thus, produces more erroneous DOA estimates. To overcome this, we propose a three-antenna BLI algorithm named as Cooperative BLI (Co-BLI) triplet which provides more TPM while maintaining high DOA estimation accuracy. This improvement comes at the cost of slight increase in implementation resources. To increase the estimation accuracy further, we next extend the proposed Co-BLI to the case of more number of antennas. For this, we also propose a way to reduce the number of antennas to form a higher order array and derive the expressions for all inter-element distances. For real-time operation, we develop a Mapping-based Cooperative Ambiguity Table (M-CAT): a look-up-table based implementation scheme where we show that by storing just a few combinations of the input ranges, one can estimate any DOA within the given field-of-view accurately, thus facilitating high throughput hardware implementation avoiding complex computations. The proposed algorithm has been validated through extensive MATLAB simulation studies and implemented in FPGA based real-time hardware.
      PubDate: FRI, 13 OCT 2023 09:16:52 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Secure Satellite–Vehicle Communications With Randomly Distributed
           Vehicles on Different Roads

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      Authors: Xiaqing Miao;Yunkang Liu;Haoxing Zhang;Hui Zhao;Shuai Wang;Gaofeng Pan;Jianping An;
      Pages: 291 - 303
      Abstract: With the development of satellite communication technology, the application of satellites in the Internet of Vehicles (IoV) system can effectively solve the limitations and deficiencies of ground communication networks. In this article, we investigate the security outage performance of a satellite–vehicle communication system consisting of a satellite ($S$), a legitimate receiving vehicle ($C$), and an eavesdropping vehicle ($E$), where $C$ and $E$ are randomly located on two different roads within the coverage area of $S$ and randomly distributed on their respective roads. The downlink communication links between $S$ and these terrestrial vehicles are assumed to suffer the shadowed-Rician fading. Then, the secrecy outage probability (SOP) is studied under the most straightforward case that $ C$ and $E$ are equipped with a single antenna. Next, we extend to the more common scenario where both $C$ and $E$ adopt multiple antennas by deriving the analytical expression for the SOP. Finally, numerical results are presented to verify our proposed analytical models.
      PubDate: FRI, 13 OCT 2023 09:16:52 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Earth-to-HAP FSO Communication With Spatial Diversity and Channel
           Correlation

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      Authors: Richa Priyadarshani;Mohamed-Slim Alouini;
      Pages: 304 - 319
      Abstract: In this article, we investigate an Earth-to-air free-space optical (FSO) system with spatial diversity, considering the influence of atmospheric turbulence, pointing error, and angle-of-arrival (AoA) fluctuations. Our study employs a high-altitude platform (HAP) as the aerial platform in the stratosphere. We begin by proposing a comprehensive mathematical channel model for the Earth-to-HAP FSO system with multiple apertures, accounting for both correlated and uncorrelated channel conditions. To verify the model's accuracy, we conduct Monte-Carlo simulations and successfully match the results. Next, we evaluate the system's performance using outage analysis, average bit error rate (BER), and asymptotic BER analysis. To gain insight into the system's behavior, we derive simplified expressions for diversity order and coding gain using asymptotic BER analysis. Through our analysis, we demonstrate the efficacy of spatial diversity in mitigating the impact of AoA fluctuations in a ground-to-HAP FSO system. In addition, we conduct a comparative analysis to assess the effect of AoA fluctuations in correlated and uncorrelated channels. Moreover, we derive the optimum receiver field of view required to design a practical ground-to-HAP FSO system capable of achieving the maximum diversity under specific AoA fluctuation conditions. Our study offers valuable insights into the potential use of spatial diversity to enhance the reliability and performance of Earth-to-HAP FSO systems, providing useful guidelines for system design and optimization.
      PubDate: THU, 19 OCT 2023 09:17:03 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Adaptive Active Anti-Vibration Control for a 3-D Helicopter Flexible
           Slung-Load System With Input Saturations and Backlash

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      Authors: Yong Ren;Zhijie Liu;Zhijia Zhao;Hak-Keung Lam;
      Pages: 320 - 333
      Abstract: This article investigates active anti-vibration control for a 3-D helicopter flexible slung-load system (HFSLS) subject to input saturations and backlash. The first target of the study is to establish a model for a 3-D HFSLS. The second target is to develop an adaptive control law for an HFSLS by analyzing its ability to compensate for the effects of input saturations, input backlash, and external disturbances, while achieving the goal of vibration reduction. Simulation results of the numerical model show that the proposed adaptive active control technology is effective in solving the oscillation suppression problem for the 3-D HFSLS with input saturations and backlash.
      PubDate: FRI, 13 OCT 2023 09:16:52 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Attitude Stabilization for a Quadrotor Using Adaptive Control Algorithm

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      Authors: Tianpeng Huang;Tieshan Li;C. L. Philip Chen;Yanan Li;
      Pages: 334 - 347
      Abstract: This article investigates the problem of attitude stabilization for a quadrotor unmanned aerial vehicle (UAV) in the presence of parametric uncertainties, control input saturation, and external disturbances. Two robust adaptive control strategies, i.e., bounded adaptive control (BAC) and asymptotical adaptive control (AAC) are developed, respectively, where the construction of saturation auxiliary system and the design of adaptive laws for parameters and disturbance's bound are considered. As a result, the compensation of input saturation, the estimation of physical parameters, and the suppression of external disturbance are achieved. In BAC, the real attitude tracking error is guaranteed to converge to a neighborhood of zero, and its transient performance is constrained to an explicit bounded range. In AAC, the real attitude tracking error is proved to be asymptotically stable, and its transient bound in terms of $L_{2}$ norm is derived by constructing an auxiliary equation, which is a computable bound. The simulation is carried out to verify the robustness and effectiveness of the proposed two control techniques.
      PubDate: FRI, 13 OCT 2023 09:16:52 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Underwater Autonomous Geolocalization Using Time Differential Polarization
           Field Against Measurement Deviations

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      Authors: Pengwei Hu;Jian Yang;Xiang Yu;Jianzhong Qiao;Lei Guo;
      Pages: 348 - 359
      Abstract: Underwater polarization (POL) fields contain spatial information about the Sun. It offers underwater POL fields the capability of navigation and positioning. Benefiting from the advantages of autonomy, the POL positioning is an effective complement for the global navigation satellite system (GNSS)-denied underwater environment. The current POL-based positioning strategies always exploit the Sun's position directly. However, the solution of geolocalization is susceptible to the constant deviations in Sun measurement. In an effort to address this issue, a two-step positioning model is designed. The Sun's positions over time are calculated by exploiting the POL field inside Snell's window, in which the underwater disturbance can be shielded. Subsequently, the first step in the two-step model adopts time-sequence Suns for coarse positioning. Around the coarse positioning solution, a local region is determined as the precondition for the second step positioning. A time differential Sun (TDS) positioning strategy is proposed for a refined geolocation. TDS exploits the Sun's dynamic movement for positioning, and thereby the position errors arising from the constant deviations are eliminated. An underwater POL positioning system is developed. The experimental results demonstrate that the positioning accuracy is improved distinctly by the TDS strategy.
      PubDate: MON, 16 OCT 2023 09:18:11 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Robust Multitarget Tracking in Interference Environments: A
           Message-Passing Approach

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      Authors: Xianglong Bai;Hua Lan;Zengfu Wang;Quan Pan;Yuhang Hao;Can Li;
      Pages: 360 - 386
      Abstract: Multitarget tracking in the interference environments suffers from the nonuniform, unknown, and time-varying clutter, resulting in dramatic performance deterioration. We address this challenge by proposing a robust multitarget tracking algorithm, which estimates the states of clutter and targets simultaneously by the message-passing (MP) approach. We define the nonhomogeneous clutter with a finite mixture model containing a uniform component and multiple nonuniform components. The measured signal strength is utilized to estimate the mean signal-to-noise ratio of targets and the mean clutter-to-noise ratio of clutter, which are then used as additional feature information of targets and clutter to improve the performance of discrimination of targets from clutter. We also present a hybrid data association, which can reason over correspondence between targets, clutter, and measurements. Then, a unified MP algorithm is used to infer the marginal posterior probability distributions of targets, clutter, and data association by splitting the joint probability distribution into a mean-field approximate part and a belief propagation part. As a result, a closed-loop iterative optimization of the posterior probability distribution can be obtained, which can effectively deal with the coupling between target tracking, clutter estimation, and data association. Simulation results demonstrate the performance superiority and robustness of the proposed multitarget tracking algorithm compared with the probability hypothesis density (PHD) filter and the cardinalized PHD (CPHD) filter.
      PubDate: FRI, 13 OCT 2023 09:16:52 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Nonmyopic Sensor Control for Target Search and Track Using a Sample-Based
           GOSPA Implementation

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      Authors: Marcel Hernandez;Ángel F. García-Fernández;Simon Maskell;
      Pages: 387 - 404
      Abstract: This article is concerned with sensor management for target search and track using the generalized optimal subpattern assignment (GOSPA) metric. Utilizing the GOSPA metric to predict future system performance is computationally challenging, because of the need to account for uncertainties within the scenario, notably the number of targets, the locations of targets, and the measurements generated by the targets subsequent to performing sensing actions. In this article, efficient sample-based techniques are developed to calculate the predicted mean square GOSPA metric. These techniques allow for missed detections and false alarms, and thereby enable the metric to be exploited in scenarios more complex than those previously considered. Furthermore, the GOSPA methodology is extended to perform nonmyopic (i.e., multistep) sensor management via the development of a Bellman-type recursion that optimizes a conditional GOSPA-based metric. Simulations for scenarios with missed detections, false alarms, and planning horizons of up to three time steps demonstrate the approach, in particular showing that optimal plans align with an intuitive understanding of how taking into account the opportunity to make future observations should influence the current action. It is concluded that the GOSPA-based, nonmyopic search and track algorithm offers a powerful mechanism for sensor management.
      PubDate: MON, 16 OCT 2023 09:18:11 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Three-Dimensional Cooperative Game Guidance Law for a Leader–Follower
           System With Impact Angles Constraint

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      Authors: Minghu Tan;Hong Shen;
      Pages: 405 - 420
      Abstract: Cooperative guidance issues with desired impact angles against a target are studied in a leader–follower system in this article. Different from the existing achievements, the attackers and the target are modeled as the participants of a pursuit-evasion game in the cooperative guidance scenario. Specifically, the leader's differential game guidance law is first formulated by using optimal control theory, while the cooperative guidance law of the follower is designed to ensure the simultaneous attack on the target with the desired impact angles by using model predictive control (MPC) and robust sliding mode control. Compared to methods using time-to-go as the consensus variable, the proposed guidance law has the potential to achieve higher performance. Simulation validation shows that simultaneous attacks can be accomplished at the desired impact angles for different targets with stationary, classical maneuvers, and game maneuvers.
      PubDate: MON, 23 OCT 2023 09:21:54 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Sequential Switching Shunt Regulation Using DC Transformers for Solar
           Array Power Processing in High Voltage Satellites

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      Authors: Carlos Orts;Ausiàs Garrigós;David Marroquí;Andreas Franke;
      Pages: 421 - 429
      Abstract: This article proposes a solar array regulation technique for a high-voltage satellite power bus. The regulation method combines on–off control at low frequency, i.e., kHz range, of highly efficient isolated and unregulated dc–dc converters operating at high frequency, i.e., hundreds of kHz. Although this technique can adopt different implementations, this article deals with a hysteretic voltage control loop at low frequency, also known sequential switching shunt regulator (S3R), and unregulated, isolated, current-fed, zero-voltage, and zero-current push–pull dc–dc converters. Design guidelines and experimental validation are provided.
      PubDate: TUE, 17 OCT 2023 09:17:39 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Inference Analysis on the Evidential Reasoning Rule Under Evidence Weight
           Variations

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      Authors: Zhi-Jie Zhou;Peng Zhang;Jie Wang;Chun-Chao Zhang;Lei-Yu Chen;Peng-Yun Ning;
      Pages: 430 - 448
      Abstract: As an important parameter in the evidential reasoning (ER) rule, evidence weight has a significant impact on the fusion results. In the existing research, much attentions have been paid on calculating or obtaining weights under different contexts, while the research on the influence and the feasible range of evidence weight is relatively insufficient. In this article, to reveal the influence of evidence weight and provide guidance for determining acceptable weight range, the impact of evidence weight on the fusion results is evaluated by two evaluation indicators, namely significant grade and fluctuation coefficient. With the analytical expression of the ER rule, the mapping relationship between weight and fusion results is established in reverse. On the premise of ensuring the effectiveness of fusion results, an optimization model for solving the acceptable weight interval (AWI) is established according to the mapping relationship. The effectiveness of the proposed method is verified by two case studies on both the multiple gyroscopes optimal decision-making and the car selection problem.
      PubDate: MON, 16 OCT 2023 09:18:11 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Sliding Mode Control Strategy of Spinning Electrodynamic Tether Formation
           During Its Spin-Up Process

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      Authors: Hongshi Lu;Changqing Wang;Aijun Li;Yuriy Mikhailovich Zabolotnov;Yong Guo;
      Pages: 449 - 462
      Abstract: This article mainly studies the spin-up control of a spinning electrodynamic tether formation (SEDTF) consisting of three linearly distributed nanosatellites connected by two tethers. The main challenge of the spin-up process is that due to the coupling effect of two tethers, more significant tether deformation and attitude disturbance on tethered nanosatellites emerge than that of a single-tether system. To deal with this problem, the dynamic model is first established for analyzing flexible tether motions and attitude motions of three nanosatellites. A sliding mode control strategy is then proposed for the spin-up process. First, considering the underactuation problem of the tether system, a sliding mode controller with an adaptive law is proposed to track spinning motion and stabilize tether deformation by adjusting only the electrical current. Second, considering the disturbance of constantly oscillating tension force, a sliding mode controller with a fixed-time prescribed performance is proposed to ensure a fast stabilization of attitude motions. Numerical results validate a synchronized spin-up of an SEDTF. Under the regulation of the proposed control strategy, tether deformations are reduced to an insignificant level, and attitude motions of nanosatellites are stabilized around designated orientations.
      PubDate: TUE, 31 OCT 2023 09:17:00 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • A Mixed Integer Linear Programming Model for Resolution of the
           Antenna-Satellite Scheduling Problem

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      Authors: Lorena Linares;Rafael Vazquez;Federico Perea;Jorge Galán-Vioque;
      Pages: 463 - 473
      Abstract: This article deals with one of the types of “Satellite Range Scheduling” problems arising in Earth Observation Satellite operations, Antenna-Satellite Scheduling. Given a set of satellites, a set of available antennas and a time horizon, the problem consists of designing an operational plan that assigns satellites to antennas in an optimal fashion. Extending a previous integer linear programming (ILP) model (shortening model, with only integer variables), we propose a mixed ILP (MILP) (shaving model, which includes both continuous and integer variables), to more efficiently solve this problem. After computing the passes generated by the satellites' windows of visibility from the antennas, the optimal planner is able to cancel a pass, move it to another antenna, or shorten its duration, in order to avoid scheduling conflicts. In contrast to the shortening model, which used intersections between passes to determine the best schedule, the shortening operation is now referred to as shaving, since the shaving model can arbitrarily adjust the duration of a pass in a razor-like fashion, giving the model its name. Computational results obtained in tests over realistic scenarios prove that the shaving model outperforms the shortening model, producing fewer cancellations, smaller shaved times, and a fairer distribution of cancelled passes among satellites, with much shorter preprocessing times.
      PubDate: MON, 23 OCT 2023 09:21:54 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Multiobjective Modular Strategic Planning Framework for Low-Altitude
           Missions Within the Urban Air Mobility Ecosystem

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      Authors: Flavia Causa;Giancarmine Fasano;
      Pages: 474 - 489
      Abstract: This article introduces a multimetric multiconstraint strategic path planning framework applicable to unstructured urban airspace. The planner is based on a modular and scalable approach to handle several information sources and aspects characterizing urban flight scenarios, such as risk and weather maps, landing site locations, navigation requirements, and mobile and fixed obstacle characteristics. This information is coupled with dynamic constraints and unmanned aerial vehicle specifications to derive a flyable and safe path connecting a start position and a destination. Strategies for data gathering and synthesis, used to keep a reduced computational burden, are described along with the path planner algorithm. The latter consists in three steps specifically developed to handle both static and time-varying information. A multi-objective cost function with variable weighting coefficients has been implemented so that the most relevant factors for the considered applications can be selected in an adaptive fashion. The performance of the developed algorithms is tested by investigating the planner behavior when changing its inputs as well as the cost function weighting coefficients, demonstrating the ability of the planner in returning an efficient and safe trajectory.
      PubDate: MON, 16 OCT 2023 09:18:11 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Uncertainties of Interpolating Satellite-Specific Slant Ionospheric Delays
           and Impacts on PPP-RTK

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      Authors: Sijie Lyu;Yan Xiang;Benedikt Soja;Ningbo Wang;Wenxian Yu;Trieu-Kien Truong;
      Pages: 490 - 505
      Abstract: Precise point positioning–real-time kinematic (PPP-RTK) achieves fast convergence in global navigation satellite system (GNSS) positioning and navigation. Correcting slant ionospheric delays is crucial for this purpose. One way to obtain the slant ionospheric corrections is by interpolating from nearby reference stations. However, the interpolation performance depends on many factors, such as time, elevation, and distance. This article aims to study the performance of three interpolation algorithms, i.e., inverse-distance weighting (IDW), radial basis function (RBF), and Kriging, under different ionospheric conditions. For inhomogeneously distributed ionospheric density, the performance of IDW is unsatisfying compared with the other two methods. In addition, we represent uncertainties on a 2-D map and find that the uncertainties increase on average linearly with distance. Elevation also plays an important role in interpolation uncertainty. Comprehensively, we build a mathematical model between the uncertainties of different interpolation methods as a function of time, elevation, and distance. They all degrade significantly with a large rate of change in the total electron content index (ROTI). Finally, we validate the proposed uncertainties by applying them to PPP-RTK processing. A convergence time within 2 min and positioning accuracy within 3.5 cm in horizontal and 6 cm in vertical directions are achieved.
      PubDate: WED, 15 NOV 2023 09:16:56 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Semisupervised Space Target Recognition Algorithm Based on Integrated
           Network of Imaging and Recognition in Radar Signal Domain

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      Authors: Chenxuan Li;Yonggang Li;Weigang Zhu;Jun Yang;Wei Qu;Yonghua He;
      Pages: 506 - 524
      Abstract: The application of deep learning to inverse synthetic aperture radar (ISAR) target recognition helps to improve accuracy in space target monitoring. However, the orbit transfer and maneuver of space targets are likely to cause range migration in radar echoes. Information deficiency of scattering points and the limitations of data acquisition methods pose enormous challenges to space target imaging and recognition. To address these issues, this article proposes a semisupervised space target recognition algorithm based on an integrated network of imaging and recognition in the radar signal domain. By directly processing radar signals, the algorithm can achieve high-precision space target imaging and recognition under the general situation and the conditions of range migrations. Based on the inherent characteristics of radar complex echoes, the algorithm utilizes unlabeled echoes to generate pseudolabels to achieve better generalization capabilities. Both real and complex convolutions are exploited to generate high-resolution features. Besides, feature optimization modules are designed to effectively integrate high-resolution texture features and contour features to magnify the difference between the target and background. The ablation experiments and contrast experiments indicate that, under the conditions of migration and missing components, the algorithm can obtain high-resolution features for imaging and recognition by only using a small amount of labeled data. Thus, the algorithm achieves high accuracy and robustness for migrated space target recognition and has superiority over other algorithms.
      PubDate: TUE, 24 OCT 2023 09:18:35 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Unlocking Conformal Microwave Split Ring Resonant Sensors for In-Flight
           Ice Sensing

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      Authors: Aaryaman Shah;Omid Niksan;Fatemeh Niknahad;Mandeep Chhajer Jain;Dan Fuleki;Faezeh Rasinarzabadi;Mohammad Hossein Zarifi;
      Pages: 525 - 536
      Abstract: In-flight ice accretion is an ongoing challenge for airplane safety as it disrupts the aerodynamics of wings, increasing drag and decreasing lift. This work proposes a conformal ice sensing method based on microwave split-ring resonator (SRR) sensors for early ice detection on critical aircraft surfaces. A single-port dual-frequency sensor with SRRs operating at ∼1.95 and ∼2.13 GHz was implemented and integrated on a NACA 0018 airfoil with a chord of 0.25 m. The sensor's performance was experimentally investigated in ice crystal icing (ICI) conditions at the National Research Council's (NRC) Research Altitude Test Facility (RATFac), at a simulated altitude of 19 000 ft, a velocity of 100 m/s and, a total air temperature of +3˚C. The performance of the sensor was evaluated with the resonant frequency parameter to extract correlations in different icing conditions. The sensor was tested in different melt ratio conditions (11%, 13%, 16%, and 25%) at varying accretion time periods, accurately determining the onset of icing, the increase in accretion volume, and its eventual shedding, while successfully differentiating the %melt conditions tested. Additionally, the sensor's response to electrothermal deicing procedures is presented, demonstrating its capability for monitoring the efficiency of deicing procedures and sensing the formation of ice during anti-icing operations.
      PubDate: MON, 30 OCT 2023 09:19:19 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Ground Facility Error Analysis and GBAS Performance Evaluation Around
           Suvarnabhumi Airport, Thailand

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      Authors: Jirapoom Budtho;Pornchai Supnithi;Nattapong Siansawasdi;Susumu Saito;Apitep Saekow;Lin M. M. Myint;
      Pages: 537 - 547
      Abstract: The performances of the ground-based augmentation system (GBAS) designed for the landing phase of aircraft rely on the accurate characterization of error models. Among various error sources, the multipath model, which is typically constructed by combining environmental errors at airports, must be modeled in GBAS. However, in practice, the multipath effects at a particular airport differ from other airports due to distinct construction sites and continually changing environments, resulting in an inaccurate error model in GBAS operations. Therefore, in this article, we develop and evaluate a 2-D ground facility error model from the Global Navigation Satellite System Stations (GNSS) at the Suvarnabhumi International Airport in Bangkok, Thailand. The results indicate that the elevation and azimuth grid points require around seven days of observation data to create the GBAS ground facility error model for GBAS operation. The number of observations per day at each elevation and azimuth grid point will determine the data requirements for the complete building of the 2-D ground error model. When the proposed model is applied to the GBAS simulation, it is found that the proposed 2-D ground error model reduces the root-mean-square deviation (RMSD) of positioning errors by around 0.4% to 3.5% when compared to the 1-D error model and the category B Ground accuracy designator model, respectively. The maximum vertical protection level reduction of the proposed 2-D B-value model in comparison with the reference 1-D B-value is 0.24 m, about a 6% reduction.
      PubDate: THU, 19 OCT 2023 09:17:02 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Targeted Detection for Attacks on the MIL-STD-1553 Bus

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      Authors: Matthew Rogers;Kasper Rasmussen;
      Pages: 548 - 557
      Abstract: Over the last decade we have observed a renewed focus on weapon systems security. Particularly the MIL-STD-1553 protocol, which was designed for military aircraft. In it, computers known as remote terminals (RTs) share information across a common serial data bus. Similarly to the well researched controller area network (CAN) bus, MIL-STD-1553 features no authentication, such that an attacker can manipulate the system by spoofing the bus controller (BC) and transmitting a single malicious message. These malicious messages are particularly bad in the MIL-STD-1553 context, where a single message can disable an RT, or engage a weapon system. To address these issues, this article proposes an intrusion detection system (IDS). While previous work utilizes the same techniques as used on the CAN bus, this leads to unnecessary complexity, inaccuracy, and poor efficiency. We take advantage of the protocol to detect an attacker spoofing the BC with 100% accuracy. In addition, we use standardized error flags to detect an attacker spoofing RT responses. The result of this work is an accurate and easy to implement detection system for all MIL-STD-1553 systems.
      PubDate: THU, 19 OCT 2023 09:17:03 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Reach-Avoid Games With Two Cooperative Attackers: Value Function and
           Singular Surfaces

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      Authors: Ruiliang Deng;Zongying Shi;Yisheng Zhong;
      Pages: 558 - 573
      Abstract: This article studies a zero-sum reach-avoid game in the 2-D plane with one defender and two cooperative attackers. The attackers try to enter the target region protected by the defender who tries to capture both of them. This work aims to obtain the value function and equilibrium strategies, for which the analysis of singular surfaces is needed. To this end, we investigate the game problem in two steps. First, a constrained game scenario is considered in which the defender needs to capture the attackers in a predefined order. The value function is constructed using geometric tools and proved using the viscosity solution method. The attackers can cooperate with each other by adopting the equilibrium strategies corresponding to the value function. Second, on the basis of the solution to the first game scenario, we analyze the general game problem in which there is no capture order restriction for the defender. The singular surfaces are discussed and the local properties of the value function in the vicinity of the singular surfaces are given. It is shown that the defender can perform better than to capture the attackers in any predefined order. Moreover, the construction of singular surfaces is illustrated with numerical results.
      PubDate: MON, 23 OCT 2023 09:21:53 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Maneuvering Target State Estimation in Range-Squared Coordinate

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      Authors: Keyi Li;Gongjian Zhou;
      Pages: 574 - 590
      Abstract: In tracking applications without angle measurements, multiple sensors are usually applied. Accurate range-Doppler (R-D) estimates with a single sensor play an important role in clutter eliminating and date fusion in multisensor target tracking. In this article, maneuvering models are first established in the range-squared (RS) coordinate, then the R-D estimation of a maneuvering target is investigated. According to the Cartesian nearly constant acceleration and constant turn models, pseudostate vectors are defined in the RS coordinate and linear time-evolution equations of pseudostate are derived to describe the corresponding maneuvers. The motion models are incorporated into the converted measurement Kalman filter to extract R-D estimates from converted measurements. Corresponding to the models, the filter initialization approach, which utilizes the prior information on motion model and kinematic parameters of target, is developed. Based on the model set in the RS coordinate and the interacting multiple model (IMM) method, a recursive estimator is developed to produce pseudostate estimates of a maneuvering target. Final R-D estimates are provided through the conversion adopting the scaled unscented transformation outside the estimation recursions to avoid the propagation of nonlinearity approximation errors. Numerical experiments in different scenarios demonstrate that the maneuvering models accurately describe the time-evolution of pseudostate and the IMM estimator achieves superior estimation accuracy and consistency thanks to the exploitation of linear motion models.
      PubDate: THU, 19 OCT 2023 09:17:03 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Low-Complexity SVD Precoding for Faster-Than-Nyquist Signaling Using
           High-Order Modulations

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      Authors: Qiang Li;Liping Li;Yingsong Li;Wenjing Han;Xingwang Li;Daniel Benevides da Costa;
      Pages: 591 - 603
      Abstract: To address the low spectrum efficiency of satellite communications, faster-than-Nyquist (FTN) signaling has proven a promising technology to improve the spectrum efficiency without requiring additional bandwidth or antennas. However, FTN signaling violates the Nyquist criterion, thereby resulting in intersymbol interference (ISI). While existing singular value decomposition (SVD) precoding can eliminate FTN-induced ISI, it ignores interblock interference, leading to low estimation accuracy. Besides, existing SVD precoding requires high complexity due to the lack of efficient and convenient implementation method. Replacing the linear convolution in FTN shaping by the circular convolution, we construct the circular FTN (CFTN) signaling and propose a CFTN-SVD precoding, which offers several advantages over the existing SVD precoding. First, the proposed CFTN-SVD precoding does not require the transmitter to acquire any accurate information about the receiver, streamlining the transmission process. Second, the proposed CFTN-SVD precoding is designed with low implementation complexity, leveraging fast Fourier transform (FFT) and inverse FFT (IFFT) to facilitate the practical implementation. Last but not least, the proposed CFTN-SVD precoding takes all FTN-induced ISI into consideration, resulting in improved estimation accuracy and making it suitable for high-order modulations. Simulation results show that compared with the bit error rate (BER) of Nyquist signaling, the BER loss of the proposed CFTN-SVD precoding is about 0.8 and 0.25 dB for uncoded and coded FTN signaling, respectively, when adopting 256-amplitude phase shift keying.
      PubDate: MON, 23 OCT 2023 09:21:53 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Tensor-Based 2-D DOA Estimation for L-Shaped Nested Array

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      Authors: Feng Xu;Hang Zheng;Sergiy A. Vorobyov;
      Pages: 604 - 618
      Abstract: Among various sensor array configurations, the L-shaped nested array offers improved performance for 2-D direction-of-arrival (DOA) estimation through co-array processing. However, conventional methods overlook the multidimensional signal structure and fail to eliminate the cross term generated from the correlated co-array signal and noise components. It leads to a significant degradation in DOA estimation performance. To deal with this problem, an iterative 2-D DOA estimation algorithm based on tensor modeling is proposed. It is capable of eliminating the cross term. Specifically, the co-array signals of virtual subarrays in orthogonal directions are derived and concatenated to construct a higher order tensor, whose factor matrices have the Vandermonde structure and preserve the interconnected azimuth and elevation information. A computationally efficient tensor decomposition method is then developed to independently estimate the azimuth and elevation angles, which are effectively paired using the spatial cross-correlation matrix. Furthermore, after investigating the cross term effect, a two-step iterative algorithm is proposed to sequentially estimate and remove the cross term based on the initial estimates obtained from the high-order tensor decomposition. Consequently, the 2-D DOA estimation with enhanced estimation accuracy, resolution, and moderate computational complexity is achieved for the L-shaped nested array. Simulation results demonstrate the superiority of the proposed algorithm over competing methods.
      PubDate: MON, 23 OCT 2023 09:21:54 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Robust Macroscopic Density Control of Microsatellite Swarm Via Local
           Measurement

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      Authors: Xiwei Wu;Bing Xiao;Lu Cao;Xiaoxiang Hu;
      Pages: 619 - 631
      Abstract: The macrohigh-level control problem of microsatellite swarm subject to safety constraints is studied in this article. The macroscopic motion dynamics of this swarm is described by the probability transition of the Markov chain with its state described by probability density distribution. Based on this model, a macroequilibrium density controller is first presented. Unlike the conventional global measurement-based control methods, this controller is implemented by using local measurement, which is provided by a decentralized counting method. The computation and the communication burden can be decreased significantly. Then, a fast temporary transition control effort is designed and added to the macroequilibrium density controller to synthesize a robust and fast macrodensity controller. It is proved that the swarm's state can asymptotically and fast converge to the desired density distribution. The key features of this robust controller are that it cannot only balance the swarm transition cost and the transition rate despite multiple constraints, but also has great robustness to initial states. The swarm control can be achieved with fast convergence rate even for the case that the microsatellites are extensively crowded at the initial. The effectiveness of this control scheme is finally verified by numerical simulation.
      PubDate: THU, 19 OCT 2023 09:17:08 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Stability Analysis of Constrained Distributed Nonlinear and Linear Kalman
           Filters for Dynamical Systems With State Constraints

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      Authors: Xiaoxu Lyu;Peihu Duan;Zhisheng Duan;Zhao Zhang;
      Pages: 632 - 643
      Abstract: This article investigates the distributed nonlinear and linear Kalman filters for dynamical systems with state equality constraints via a sensor network, where each sensor estimates the system states by utilizing its own and neighbors' information. First, a constrained distributed extended Kalman filter is proposed, and its stability is proven under feasibility conditions. Then, this constrained distributed extended Kalman filter is relaxed to the linear form, obtaining more precise results under weaker conditions. Moreover, a local constraint fusion algorithm is proposed. Finally, the effectiveness of the proposed filters is demonstrated by two simulation examples.
      PubDate: TUE, 31 OCT 2023 09:17:00 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Graph-Based Semantic Embedding Refinement for Zero-Shot Remote Sensing
           Image Scene Classification

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      Authors: Junyuan Shang;Chang Niu;Wenlve Zhou;Zhiheng Zhou;Junmei Yang;
      Pages: 644 - 657
      Abstract: Zero-shot remote sensing image scene classification (ZS-RSISC) aims to identify remote sensing (RS) image scenes of unseen classes whose samples are unavailable in the training stage. To transfer knowledge from seen RS classes to unseen RS classes, existing methods either rely on laborious manual labeling to learn semantic features or directly use the word embeddings learned based on the general corpus and independently of zero-shot models. They ignore the complex interclass correlation information, which plays a vital role in communicating seen with unseen classes. Besides, current studies in ZS-RSISC impose the same penalty to equally constrain each class for the interclass separation and intraclass compactness, which results in unclear classification boundaries. In this article, we tackle ZS-RSISC via graph-based semantic embedding refinement (GSER) in an end-to-end manner. We propose semantic graph convolutional networks (S-GCNs) to explore the correlation structure among classes in a unified framework. The semantic graph embeddings are further refined by the learning of the semantic-guided class patterns and component patterns. Specifically, we propose adaptive additive separation (AAS) loss to adaptively adjust the appropriate penalty for each class and explicitly promote intraclass compactness and interclass separation. Further, instance-level alignment and class-level alignment are proposed to enhance the discriminative ability of the semantic-guided class patterns. To alleviate model bias toward seen classes, semantic-guided component patterns shared by seen and unseen classes are exploited via feature reconstruction. Extensive experiments of both the zero-shot and generalized zero-shot settings demonstrate the effectiveness of our proposed GSER.
      PubDate: MON, 23 OCT 2023 09:21:53 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • GGA-EMD-Based Inversion Method of Spectrum Reflectance Template for
           Celestial Doppler Difference Navigation

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      Authors: Jin Liu;Zhou-Qian Xiang;Xiao-Lin Ning;Jian-Cheng Fang;
      Pages: 658 - 674
      Abstract: The Doppler difference velocity between the measured solar spectrum and the measured asteroid spectrum is the measurement of the celestial Doppler difference navigation. Due to the absorption effect of the asteroid on the measured asteroid spectrum, the Doppler difference velocity is inevitably affected. In order to solve this problem, considering the fact that the asteroid reflectance is the ratio of the approximation of the asteroid spectrum to that of solar spectrum, we utilize the Gaussian genetic algorithm (GGA) and empirical mode decomposition (EMD) to extract the approximation of celestial spectra and propose an inversion method of celestial spectrum reflectance template based on GGA-EMD. First, the celestial spectrum is decomposed by EMD into the intrinsic mode functions (IMFs). A certain combination of IMFs can embody the spectrum reflectance. Then, we utilize the undetermined spectrum reflectance function and the measured solar spectrum to produce the virtual asteroid spectrum and adopt the Taylor method to estimate the Doppler difference velocity between the measured asteroid spectrum and the virtual one. Finally, to converge fast, we utilize the Gaussian function to optimize the mutation and crossover operations of genetic algorithm (GA) and propose the GGA. Using the Doppler difference velocity errors as the object of the fitness function, the GGA optimizes the combination of IMFs, which is used to construct an optimized celestial spectrum reflectance template. The experiment results show that the inversion method of celestial spectrum reflectance template based on GGA-EMD constructs the spectrum reflectance template effectively and improves the accuracy of the Doppler difference velocity.
      PubDate: TUE, 24 OCT 2023 09:18:35 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Large Array Antenna Aperture for GNSS Applications

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      Authors: Noori BniLam;Fabio Principe;Paolo Crosta;
      Pages: 675 - 684
      Abstract: In this article, we propose a systematic procedure that exploits the available antennas to achieve a large array antenna aperture. The proposed procedure utilizes the coarray concept to provide a large virtual array antenna, thus achieving a high beamforming gain and a high spatial resolution. The procedure also employs an Angle-of-Arrival (AoA) evaluation step in order to detect the array antenna phase ambiguity, which might occur due to the random nature of the selected antennas' distribution. The procedure is generic and application agnostic; however, we have adopted here the Global Navigation Satellite System. The performance of every selected array antenna has been analyzed, theoretically, using the Cramer–Rao Lower Bound calculations. In addition, simulation and experimental analyses have also been performed to verify the performance of the selected array antennas. All the provided analyses show that the selected array antennas, using the proposed procedure, outperform the traditional planar array antennas.
      PubDate: TUE, 24 OCT 2023 09:18:35 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Framework and Methods of State Monitoring-Based Positioning System on
           WIFI-RTT Clock Drift Theory

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      Authors: Xiaochen Guo;Haitao Wu;
      Pages: 685 - 697
      Abstract: High-precision indoor positioning problems have attracted considerable attention recently. The indoor ranging and positioning method based on wireless fidelity (WIFI) round-trip-time has become popular. However, resulting from clock drift (CD), there are two problems that are still not completely resolved and need to be addressed. The first problem is ranging error drift, namely the reset of phase distortion errors (PDEs), while the second problem is the communication state change, namely ranging information loss and restoration. To solve these two problems, this study noticed that the reset of PDEs will only change along with the changes in communication states. Thus, a monitoring-based positioning system (MPS) framework is presented based on the theoretical model of CD as a method to account for ranging error drift and communication state change when estimating position with the IEEE 802.11mc protocol. Then, a state monitoring algorithm (SMA) suitable for the MPS framework is proposed to accurately capture the moment of communication state change. Further, a PDE constraint (PDEC) model is proposed based on the SMA to estimate PDEs in different communication states to reduce the impact of CD on positioning results. The experimental results finally show that considerable improvement in positioning accuracy can be obtained using the SMA and PDEC model compared with that obtained using the semiparametric solution algorithm.
      PubDate: WED, 25 OCT 2023 09:16:53 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Measurement Combination Estimator for Multisensor Extended Object Tracking
           Using Random Matrix

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      Authors: Xiaoxiao Zhang;Jian Lan;
      Pages: 698 - 715
      Abstract: For extended object tracking (EOT), the random-matrix approach has received extensive attention. It is able to jointly estimate the kinematic state and extension of an extended object. Multiple sensors with different characteristics and perspectives can provide new information especially for EOT. However, how to fuse the measurements from multiple sensors to guarantee the improvement of the estimation accuracy of the state and extension still needs further research. This article proposes a random-matrix approach to elliptical EOT using multiple sensors based on a centralized fusion architecture. In order to handle the different distortions of multiple sensors, a measurement combination (MC) approach is first proposed. As a result, a likelihood function with a proper form is obtained, and it can also preprocess the multisensor measurements and take the scattering matrix of measurement means into consideration. Based on the likelihood function, the MC estimator (MCE) is derived for multisensor EOT in a Bayesian framework. It inherits the analytical and concise scheme of the single-sensor EOT approach. Based on the proposed MCE approach, it is theoretically proved that the estimation accuracy of the state and extension can be improved when the sensor number increases. Effectiveness of the MCE approach is demonstrated by simulation and experimental results compared with the existing multisensor EOT approaches using random matrix.
      PubDate: WED, 01 NOV 2023 09:18:16 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Barrier Function-Based Backstepping Fractional-Order Sliding Mode Control
           for Quad-Rotor Unmanned Aerial Vehicle Under External Disturbances

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      Authors: Bowen Liu;Yingxun Wang;Omid Mofid;Saleh Mobayen;Mohammad Hassan Khooban;
      Pages: 716 - 728
      Abstract: The main control objective for the quad-rotor system is the attitude and position tracking control which is accomplished in this article using the backstepping fractional-order sliding mode control approach combined with the adaptive tuning based barrier function theory. The quad-rotor's dynamical model is obtained at the appearance of the disturbance that is entered to the quad-rotor system by an exterior force. Hence, the attitude and position tracking errors between the quad-rotor's actual states and their desired trajectory are defined. Afterward, the fractional-order sliding surfaces are defined to guarantee finite-time reachability of the defined tracking error. The designed fractional-order surface eases the control process due to the removal of the high-derivative of virtual controllers. Then, the extended adaptive barrier function laws named fractional-order-based adaptive barrier function laws are employed to eliminate the knowledge of the upper bounds of external disturbances. The Lyapunov theory concept mixed with the backstepping control strategy is used to demonstrate that the fractional-order sliding surface reaches the neighborhood of origin in the finite time. Finally, simulation results in various scenarios on the MATLAB/ Simulink environment are provided to show that the designed control procedure in this article is an efficient control technique to assure that attitude and position's trajectories of the quad-rotor system track desired trajectories properly in the existence of the external disturbances. Moreover, the designed method is compared to the existing method to validate the efficiency of the proposed method. Lastly, experimental results using the Speedgoat real-time target machine is implemented to assure validity of the recommended method.
      PubDate: THU, 02 NOV 2023 09:17:12 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • A Bayesian Multistage Fusion Model for Radar Antijamming Performance
           Evaluation

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      Authors: Linqi Zhao;Liang Yan;Xiaojun Duan;Zhengming Wang;
      Pages: 729 - 740
      Abstract: When evaluating the effectiveness of radar antijamming techniques, the limited sample size poses a challenge. To overcome this limitation, this article proposes a signal-to-interference ratio fusion (SIRF) model that integrates multistage data. The SIRF model is based on the relationship between the signal-to-interference ratio and radar target range, considering three fluctuation models for radar cross section: Swerling I-II, Swerling III-IV, and Rayleigh. By employing the Bayesian theory and the antientropy weight method, the SIRF model enables the fusion of data collected from various stages, including mathematical simulations, hardware-in-the-loop tests, and field tests. Numerical results demonstrate that the proposed SIRF model exhibits superior consistency and robustness compared to the conventional fusion model based on the beta distribution.
      PubDate: TUE, 31 OCT 2023 09:17:01 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Fixed-Wing Unmanned Aerial Vehicle Rotary Engine Anomaly Detection via
           Online Digital Twin Methods

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      Authors: Chao-Chung Peng;Yi-Ho Chen;
      Pages: 741 - 758
      Abstract: Anomaly detection based on data-driven methods is an applicable way to deal with the complex structure of aircraft engine. In this article, certain existing data-driven methods are first introduced for model construction of a fixed-wing unmanned aerial vehicle (UAV) rotary engine. However, due to the prediction transient response and the associated stability not being guaranteed, a hybrid observer/Kalman filter identification (OKID) scheme is proposed. The presented method uses autoregressive model with exogenous inputs (ARX) model for modeling and involves a deadbeat observer design, which can allow model outputs to converge to real output in a theoretical proof. The identified models are seen as the digital twins of a healthy system, which can be taken as a reference to monitor the status of the UAV rotary engine. For comparison study, three data-driven methods, including neural network (NN), fast orthogonal search (FOS), and the proposed OKID hybrid model, are assessed by their model accuracy, stability, and convergence through practical flight data. Experimental results show that the developed method is the best alternative for online fault detection even in the face of limited training data. Moreover, given the real test flight data, the proposed OKID hybrid model can identify the anomaly status and figure out the abnormal part of the fixed-wing rotary engine, which greatly contributes to field managers for maintenance policy decision-making.
      PubDate: MON, 06 NOV 2023 09:19:33 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Joint Beamforming Design and Power Control Game for a MIMO Radar System in
           the Presence of Multiple Jammers

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      Authors: Jiale Wu;Chenguang Shi;Weiwei Zhang;Jianjiang Zhou;
      Pages: 759 - 773
      Abstract: In this article, the problem of joint beamforming and power allocation in a multiple-input multiple-output (MIMO) radar network is investigated in the presence of signal-dependent interference and multiple jammers. Specifically, the main objective of the radar system is to improve the performance in terms of total power consumption and beampattern peak sidelobe level (PSL) under the signal-to-interference-plus-noise ratio (SINR) constraints at the radars, whereas the jammers aim to maximize their side effects on the radars. The interactions between the radar system and the jammers are formulated as a noncooperative game. An efficient iterative algorithm is proposed to minimize the total weighted transmit power of the radar system based on the Nash equilibrium of the game. In addition, in order to suppress the sidelobe level of both the transmit and receive beampatterns for the radars, this article further studies the problem of minimizing the sidelobe level by introducing a pricing mechanism that takes into account the accumulated sidelobe level of the radar system. The sidelobe control problems are effectively solved using the Lagrange duality and the Lagrange multiplier method. Finally, simulation results are provided to confirm the convergence of the proposed algorithms and assess the performance in terms of total power consumption and PSL.
      PubDate: TUE, 31 OCT 2023 09:17:00 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Joint Beampattern Design and Online Route Planning for Multitarget
           Tracking in Airborne Radar System

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      Authors: Lintao Ding;Chenguang Shi;Jianjiang Zhou;
      Pages: 774 - 788
      Abstract: Airborne colocated multiple-input multiple-output (C-MIMO) radar system has been validated to offer improved multitarget tracking (MTT) capabilities. By combining the beampattern design and observation position optimization of airborne C-MIMO radar with traditional resource allocation strategies, higher degrees of freedom can be obtained, leading to resource utilization efficiency and performance enhancements. In this article, a joint beampattern design and online route planning (JBD-ORP) strategy is proposed for MTT in airborne C-MIMO radar system under jamming environments. The key mechanism of the proposed strategy is to collaboratively coordinate the waveform correlation matrix (WCM), kinematic velocity and heading angle of the airborne radar, in order to improve MTT performance under the constraints of maneuverability limitations and system resource budgets. The predictive Bayesian Cramér–Rao lower bound is derived and adopted as the metrics to characterize the target tracking accuracy performance. As the formulated JBD-ORP problem is nonlinear and nonconvex, we approach it differently depending on whether prior information about interference source is known and propose a partition-based three-stage approach to solve it effectively. Simulation results show that the proposed JBD-ORP strategy achieves the best system performance in comparison with other benchmarks.
      PubDate: TUE, 31 OCT 2023 09:17:00 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • High-Precision Trajectory Tracking Control for Free-Flying Space
           Manipulators With Multiple Constraints and System Uncertainties

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      Authors: Guangtai Tian;Bin Li;Qin Zhao;Guangren Duan;
      Pages: 789 - 801
      Abstract: Precise motion control for free-flying space manipulators (FFSMs) plays an important role in space missions. However, system uncertainties and various physical constraints severely degrade the trajectory tracking performance. In order to tackle these difficulties, a fully actuated system approach (FASA)-based composite controller is developed, which consists of a nonlinear disturbance observer (NDO) in the inner loop and a high-precision trajectory controller in the outer loop. More specifically, the NDO is designed for tackling system uncertainties. Moreover, a gradient-based optimal parameter tuning method is developed for tuning the control gains of the composite controller. The satisfaction of physical constraints, which include angular constraints and actuator constraints can be guaranteed by the gradient-based optimal parameter tuning method. Therefore, the high-precision trajectory tracking performance, optimal control gains, angular constraints, and actuator constraints can be ensured simultaneously. Simulation results are presented to demonstrate the effectiveness of the proposed method.
      PubDate: TUE, 31 OCT 2023 09:17:00 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Interacting Multiple-Mode Estimation Using Centroid Fixed Structure for
           High Precision

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      Authors: Guowei Li;Qiang Wang;Shurui Zhang;Weixing Sheng;Thia Kirubarajan;
      Pages: 802 - 818
      Abstract: Model set design is an important area for multiple-mode estimation, and its main purpose is to design a suitable model set to improve the accuracy and stability of target tacking. In this article, a novel multiple model estimation algorithm, namely, centroid fixed structure of interacting multiple-model estimation (CFIMM), is proposed to obtain the characteristic of the high precision and strong stability. First, the minimum distance method and the minimum model set method are, respectively, provided. Then, based on those two methods, the centroid model set design method is proposed with three different approaches to split the centroid. It proves that the centroid model set has minimal mathematical expectations with the actual models, when the unknown real model space is very large and even uncountable. Finally, the processing of the proposed CFIMM algorithm based on the centroid model set design method is discussed in detail. The proposed CFIMM holds not only the advantages of centroid model set but also the characteristic of the high precision and strong stability. The simulations of CFIMM highlight the correctness and effectiveness of the proposed methods.
      PubDate: MON, 30 OCT 2023 09:19:19 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Neural-Network-Based Nonlinear Optimal Terminal Guidance With Impact Angle
           Constraints

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      Authors: Lin Cheng;Han Wang;Shengping Gong;Xu Huang;
      Pages: 819 - 830
      Abstract: The terminal guidance problem considering nonlinearity, optimality, and impact angle constraints is investigated. First, the conditions for optimal guidance in the longitudinal plane are derived based on the Pontryagin's maximum principle, and then the to-be-solved two-point boundary value problem is equivalent to a backward integration problem. Then, analytical boundaries are given to initialize the states for backward integration. Based on the easily accessible dataset, a neural network is trained to approximate the optimal guidance commands. Lastly, an optimal terminal guidance scheme combined with the neural network and a biased proportional navigation guidance is proposed. Compared with the existing terminal guidance methods, the proposed guidance strategy balances the performances about flight optimality, on-board implementation capability, and impact angle satisfaction when high dynamical nonlinearity is considered. Simulations are given to validate the effectiveness of the proposed techniques, and demonstrate the advantages of the algorithm on optimality, real-time performance, and impact angle satisfaction in nonlinear cases.
      PubDate: MON, 30 OCT 2023 09:19:19 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • State Constrained Fault-Tolerant Control of Hypersonic Vehicle With
           Unknown Centroid Shift Based on Zero-Sum Game

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      Authors: Hui Ye;Yizhen Meng;Liyan Wen;Zeqiao Li;
      Pages: 831 - 843
      Abstract: For hypersonic vehicle (HSV), this research provides a unique fault-tolerant control (FTC) strategy that keeps the aircraft stable and safe even in the face of an unknown centroid offset. In terms of the challenges of variation of the inertial matrix, eccentric moment, and coupling system uncertainty deriving from centroid shift, a time-varying state-constraint function is created to constrain the motion state of the aircraft for ensuring safety. Furthermore, a value function based on adaptive dynamic programming is constructed to represent the distance between the system state and the constraint boundary in the security constraint domain. This value function is used to construct a zero-sum game with the optimal control of the system to achieve optimal error FTC of the state constraint of the aircraft. By using the backstepping control framework and Lyapunov stability theory, it is confirmed that the proposed FTC scheme ensures that all the closed-loop signals are bounded. Ultimately, simulations are carried out to demonstrate the benefits of the proposed FTC strategy.
      PubDate: FRI, 17 NOV 2023 09:19:49 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Trajectory Correction of the Rocket for Aerodynamic Load Shedding Based on
           Deep Neural Network and the Chaotic Evolution Strategy With Covariance
           Matrix Adaptation

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      Authors: Guanghui Cheng;Yudong Hu;Wuxing Jing;Ruoming An;Changsheng Gao;
      Pages: 844 - 856
      Abstract: This article studies the problem of trajectory correction optimization for aerodynamic load shedding in the rocket's ascending phase considering the wind. Based on the statistical horizontal wind field, the weakest wind field is proposed to plan the nominal trajectory and the strongest wind field is proposed to test the performance of the designed trajectory. Considering the weakest wind field, two time-varying correction coefficients are calculated by one deep neural network unlike the traditional methods, and used to plan the rocket's flight attitudes when optimizing the rocket's trajectory. There are a large number of parameters to be optimized in this problem, so the traditional trajectory optimization techniques may suffer from poor convergence issues. By introducing the chaotic function into the traditional evolution strategy with covariance matrix adaptation, a novel variant C-CMA-ES is proposed to solve the trajectory optimization problem, and its efficiency is demonstrated by some popular test functions. Besides, the cost function to be minimized is shaped based on the rocket's maximal normal aerodynamic load and final state deviations. Finally, compared with the other three strategies, the efficiency of the proposed trajectory correction strategy is demonstrated by multiple simulation scenarios considering the strongest wind field and the Monte Carlo method considering the random wind fields.
      PubDate: THU, 09 NOV 2023 09:16:41 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Trajectory Planning for Spacecraft Formation Reconfiguration Using
           Saturation Function and Difference-of-Convex Decomposition

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      Authors: Zichen Zhao;Haibin Shang;
      Pages: 857 - 866
      Abstract: The trajectory planning for spacecraft formation reconfiguration (SFR) presents significant technical challenges due to its time-optimal performance index, highly nonlinear terminal formation constraints, potential large-scale spacecraft deputies, and significant requirements for robustness and efficiency in planning. This issue is addressed within the framework of sequential convex programming (SCP) due to its rapid computational capabilities, coupled with two key techniques to enhance SCP's hard-to-ensure convergence. First, to effectively utilize the concave-convergent characteristic of employing SCP to handle pure convex or concave functions, the problem is transformed into an equivalent difference of convex (DC) form. This results in a problem where all components are either convex or concave. A semidefinite problem is constructed to optimize the DC decomposition procedure, thereby achieving fast, reliable, and generalized transformation. Second, saturation functions are then employed to expand the feasible region of the DC problem, overcoming the artificial infeasibility commonly encountered in traditional SCP. A series of bijective mappings are used to connect the saturation function with dissatisfaction across all constraints. By penalizing the saturation function, the SCP procedure can be directed toward optimal solutions. Through rigorous theoretical derivations and sufficient numerical verifications, it can be confirmed that the combination of DC decomposition and saturation function performs exceptionally well in ensuring the convergence of SCP, contributing to the rapid and reliable generation of time-optimal SFR trajectories.
      PubDate: THU, 02 NOV 2023 09:17:12 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Finite-Time Neural Optimal Control for Hypersonic Vehicle With AOA
           Constraint

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      Authors: Xiao Han;Bo Wang;Lei Liu;Huijin Fan;
      Pages: 867 - 881
      Abstract: For an air-breathing hypersonic vehicle (AHV), the high angle of attack (AOA) caused by longitudinal maneuver may lead to the inlet unstart of a scramjet. Therefore, it is necessary to consider the AOA constraint in the controller design of the AHV. In this article, a finite-time neural optimal controller is proposed to satisfy the tracking performance and the AOA constraint simultaneously. First, a basic tracking controller is built to ensure the finite-time convergence of tracking error. Then, a control-oriented safety quantization is investigated. A specific barrier function based on the safety margin is constructed to quantify the safety risk of the AOA. To enforce the AOA constraint in a minimally invasive fashion, a safe adaptive dynamic programming (ADP) is proposed to optimize the basic controller automatically. The specific barrier function is treated as an extended state and, thus, incorporated into the value function of ADP as a risk penalty term for the tradeoff between the tracking performance and the AOA safety constraint. Finally, a safety-considered policy iteration and a single-critic neural network are developed to build an adaptive AOA safety modified policy online. The comparison simulation results show the effectiveness of the proposed controller.
      PubDate: THU, 09 NOV 2023 09:16:41 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Composite Controller Design for Quadrotor UAVs With Uncertainties and
           Noises Based on Combined Kalman Filter and GPIO

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      Authors: Ting Li;Zhenhua Zhao;Shihong Ding;Jinya Su;
      Pages: 882 - 892
      Abstract: This article investigates the attitude control problem of quadrotor unmanned aerial vehicles (UAVs) with disturbances, actuator faults, and measurement noises. First, to avoid the noise amplification, the attitude system is transformed into a linear decoupled system, and the influences of uncertainties (including disturbances, actuator faults, and nonlinearities) are regarded as lumped disturbances. Second, to handle the uncertainties and noises simultaneously, the combined Kalman filter generalized proportional integral observer (KFGPIO) is introduced to estimate the filtered states and lumped disturbances. And then, a composite controller is proposed based on the estimation of KFGPIO. Simulation results validate that the proposed method achieves good disturbance rejection and fault tolerance performance and guarantees the continuity of control action even under serious measurement noises.
      PubDate: FRI, 03 NOV 2023 09:17:11 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Ambiguity-Free 2-D DOA and Polarization Estimation for Mirrored Linear
           Crossed-Dipole Arrays

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      Authors: Hao Nan;Xiaofeng Ma;Minghui Dai;Shuang Qiu;Yubing Han;
      Pages: 893 - 906
      Abstract: Normally, linear crossed-dipole arrays (LCDA) can only perform 1-D direction of arrival (DOA) estimation. In this article, a prior known mirror reflection structure is added for LCDA and a mirrored LCDA (MLCDA) capable of 2-D DOA estimation is constructed. The joint DOA and polarization parameter estimation performance of MLCDA is significantly improved due to the added mirror reflection structure. First, it is proven that an MLCDA with half wavelength interelement spacing (IES) can perform ambiguity-free joint 2-D DOA and polarization estimation when the array is neither parallel nor perpendicular to the added mirror reflection structure. Then, the closed-form IES boundary for ambiguity-free 2-D DOA and polarization estimation is derived. The parameter estimation accuracy and multitarget resolution can be further enhanced by increasing the IES under such a boundary. Moreover, a computationally efficient compressed mirrored dimension-reduction multiple signal classification (C-MDR-MUSIC) algorithm for MLCDA is proposed. Initially, a dimension-reduction processing for the manifold vector of MLCDA is achieved. Then, the range of spatial spectrum search required for MDR-MUSIC is halved, employing the conjugate symmetry property between the spatial steering vectors of the incident and reflected signals. Numerical simulations are conducted to validate the performance of MLCDA and the proposed C-MDR-MUSIC algorithm.
      PubDate: MON, 06 NOV 2023 09:19:34 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Performance Analysis and Path-Planning for Self-Energized UAV-Assisted
           Relay Networks

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      Authors: Mohamed A. Aboulhassan;Ahmed H. Abd El-Malek;Anas M. Salhab;Salam A. Zummo;
      Pages: 907 - 917
      Abstract: This article studies performance analysis for a novel unmanned aerial vehicles (UAVs) selection algorithm for UAV-assisted relay networks. Several UAVs are located randomly between the transmitter and receiver base stations in the proposed model. Assuming no direct link between the transmitter and receiver, only one UAV is selected to act as a relay. Furthermore, we assume that the UAVs are energized from a dedicated power base station. The UAV selection mechanism is performed over two phases. In phase one, the UAVs that have succeeded in harvesting energy greater than a predefined threshold are eligible to be selected. Whereas in phase two, the UAV with the highest signal-to-noise ratio is selected to act as a relay. We derive closed-form expressions for the total outage probability, average throughput, and average symbol error probability under several practical assumptions, such as the nonlinear energy harvesting model, random UAV locations, and Nakagami-$m$ fading channel models. Moreover, we propose a UAV localization approach using deep reinforcement learning (DRL) for the selected UAV. The localization problem is found to be nonconvex. Thus, a DRL approach is used to find the optimum UAV trajectory. The simulation results reveal that the proposed localization and selection approaches outperform conventional techniques in the literature. Furthermore, findings show the practicality and validity of the derived closed-form expressions.
      PubDate: MON, 13 NOV 2023 09:18:16 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Neural-Based Compression Scheme for Solar Image Data

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      Authors: Ali Zafari;Atefeh Khoshkhahtinat;Jeremy A. Grajeda;Piyush M. Mehta;Nasser M. Nasrabadi;Laura E. Boucheron;Barbara J. Thompson;Michael S. F. Kirk;Daniel E. da Silva;
      Pages: 918 - 933
      Abstract: Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to make. Stronger compression methods, by distorting the data, can increase data throughput at the cost of accuracy, which could affect scientific analysis of the data. On the other hand, preserving subtle details in the compressed data requires a high amount of data to be transferred, reducing the desired gains from compression. In this work, we propose a neural network-based lossy compression method to be used in NASA's data-intensive imagery missions. We chose NASA's Solar Dynamics Observatory (SDO) mission, which transmits 1.4 TB of data each day as a proof of concept for the proposed algorithm. In this work, we propose an adversarially trained neural network, equipped with local and nonlocal attention modules to capture both the local and global structure of the image resulting in a better tradeoff in rate-distortion (RD) compared with conventional hand-engineered codecs. The RD variational autoencoder used in this work is jointly trained with a channel-dependent entropy model as a shared prior between the analysis and synthesis transforms to make the entropy coding of the latent code more effective. We also studied how optimizing perceptual losses could help our neural compressor to preserve high-frequency details of the data in the reconstructed compressed image. Our neural image compression algorithm outperforms currently-in-use and state-of-the-art codecs, such as JPEG and JPEG-2000, in terms of the RD performance when compressing extreme-ultraviolet (EUV) data. As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole detection using our compressed images, and generated consistent segmentations, even at a compression rate of $\sim\! 0.1$ bits per pixel (compared with 8 bits per pixel on the original data) using EUV data from SDO.
      PubDate: MON, 13 NOV 2023 09:18:16 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Feature-Level Fusion Recognition of Space Targets With Composite
           Micromotion

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      Authors: Yuanpeng Zhang;Yan Xie;Le Kang;Kaiming Li;Ying Luo;Qun Zhang;
      Pages: 934 - 951
      Abstract: For space targets with the same shapes and composite relationships in terms of micromotion forms (STSSCRMFs), the accuracy of deep learning approaches with single-domain data is seriously degraded. To address this issue, this article proposes a hybrid neural network based on feature-level fusion that utilizes multidomain radar information to recognize STSSCRMFs. The proposed network simultaneously processes radar cross-section (RCS) time series, high-resolution range profile (HRRP) sequences, and time-frequency (TF) spectrograms through three branches, leveraging their complementary characteristics. Additionally, an attention mechanism is incorporated into the feature-level fusion module to selectively enhance the features of important domains, thereby achieving improved space target recognition. Consequently, the temporal-spatial features of multidomain data are adaptively fused, capturing discriminative and complementary representations, which effectively enhances the accuracy of space target recognition. Experimental results demonstrate the superiority and effectiveness of the proposed method.
      PubDate: FRI, 10 NOV 2023 09:16:51 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Target Localization With Bistatic MIMO and FDA-MIMO Dual-Mode Radar

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      Authors: Yunfei Fang;Shengqi Zhu;Bin Liao;Ximin Li;Guisheng Liao;
      Pages: 952 - 964
      Abstract: In this article, we investigate the problem of target localization with a multiple-input–multiple-output (MIMO) and frequency diverse array MIMO (FDA-MIMO) dual-mode radar system. The signal model for this newly introduced radar system is presented in detail. On this basis, a computationally efficient method for joint angle and range estimation is proposed by taking advantage of the subspace principle. First, the direction-of-arrival (DOA) of each target is determined by utilizing the fused signal data received via this dual-mode radar. In order to solve the coupling of target range and direction-of-departure (DOD) in FDA-MIMO radar mode, we use the monostatic-like characteristics of the dual-mode radar to develop an effective decoupling method. More specifically, by employing the same eigenmatrix corresponding to MIMO and FDA-MIMO radar data, the coupling of DOD and range in the FDA-MIMO mode can be easily overcome, and the parameters can be successively estimated with automatic paring. Further, to make full use of the transmit array for DOD estimation, we propose to compensate the range information in the transmit array steering vector to achieve an improved estimate of the target DOD. Extensive simulation results are performed to show that the proposed approach can provide superior DOD and range estimation accuracy while maintaining DOA estimation performance under the presented dual-mode radar system, and does not require the design of additional parameter pairing strategies with low computational cost, exhibiting excellent target localization behavior.
      PubDate: FRI, 17 NOV 2023 09:19:49 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Recursive Nonlinear Filtering via Gaussian Approximation With Minimized
           Kullback–Leibler Divergence

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      Authors: Liping Guo;Sanfeng Hu;Jie Zhou;X. Rong Li;
      Pages: 965 - 979
      Abstract: In order to solve various problems in a Bayesian framework efficiently, it is critical to approximate a posterior distribution. This work provides a Gaussian approximation of a general distribution via Kullback–Leibler divergence minimization by deterministic sampling. Two algorithms, feasible direction method and linearized alternating direction method of multipliers, each having its strengths, are proposed for the Gaussian approximation. Theoretical results of complexity, convergence, convergence rate, and guidelines for parameter selection of the proposed algorithms are also provided. Based on the Gaussian approximation, two recursive filters are developed for nonlinear dynamic systems. Examples are given to demonstrate the effectiveness and efficiency of the proposed Gaussian approximation and the related filters.
      PubDate: TUE, 07 NOV 2023 09:17:30 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Time-Modulated Arrays in Scanning Mode Using Wideband Signals for
           Range-Doppler Estimation With Time-Frequency Filtering and Fusion

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      Authors: Yue Ma;Chen Miao;Weijun Long;Ruoyu Zhang;Qiaoyu Chen;Jindong Zhang;Wen Wu;
      Pages: 980 - 990
      Abstract: Time-modulated array (TMA) has garnered significant interest in recent years as an alternative to phased arrays for certain applications. The TMA offer advantages such as simplicity and high accuracy when employed in scanning mode. However, a significant limitation of TMA is the constrained signal bandwidth, which must be lower than the modulation frequency to avoid aliasing. This constraint can impede progress in wideband range-Doppler (RD) estimation. To address this problem, this article proposes a TMA structure and signal processing method that integrates time, frequency, and time–frequency (TF) domains to extract information for RD estimation. The proposed methods effectively overcome the bandwidth limitation by utilizing TF filtering and data fusion techniques, thereby enhancing the performance of RD estimation in TMA. The simulation results validate the effectiveness of the proposed strategies.
      PubDate: THU, 09 NOV 2023 09:16:41 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • GNSS-Denied Joint Cooperative Terrain Navigation and Target Tracking Using
           Factor Graph Geometric Average Fusion

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      Authors: Hallysson Oliveira;Stiven S. Dias;Marcelo G. S. Bruno;
      Pages: 991 - 1005
      Abstract: We propose a fully distributed methodology based on factor graphs for joint cooperative localization and distributed noncooperative target tracking in a 3-D scenario where multiple surveillance aircraft fly in formation without access to global navigation satellite system (GNSS) measurements or communication with anchor nodes. Our approach is based on the adapt-then-combine (ATC) diffusion scheme, which is integrated into the factor graph by the introduction of special combine factors to perform geometric average fusion of the target beliefs over a partially connected network. The updated target belief held by each aircraft following the combine step is also fed back to improve the aircraft's own self-localization, assimilating the target measurements. Simulation results show that the proposed distributed algorithm performed close to the posterior Cramér-Rao lower bound of the optimal centralized solution and that the agents approached a consensus about the target state estimate.
      PubDate: FRI, 17 NOV 2023 09:19:49 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Target Measurement Performance of Distributed MIMO Radar Systems Under
           Nonideal Conditions

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      Authors: Yuanyuan Liang;Gongjian Wen;Lingxiao Zhu;Dengsanlang Luo;Haibo Song;Yangliu Kuai;
      Pages: 1006 - 1022
      Abstract: Evaluating target measurement performance is significant for the design and application of distributed multiple-input multiple-output (MIMO) radar. Existing research into the target measurement performance was mostly conducted under ideal conditions. In practice, however, distributed MIMO radar may inevitably suffer from the system errors in time sync, frequency sync, and beam pointing as well as the uncertainties in sensor positions. These nonideal factors may lead to a degradation of the target estimation performance to some extent, and this joint impact remains unclear. Motivated by this, in this article, we systematically explore the target measurement performance of distributed MIMO radar under nonideal conditions. First, we formulate a general system model for distributed MIMO radar incorporating the errors in time sync, frequency sync, sensor position, and beam pointing. By treating these system errors as Gaussian random variables, we form the hybrid Cramér-Rao Bound (HCRB) for the joint estimation of the target parameters and these errors. The derived HCRB is shown to be effective in lower bounding the mean-square error (MSE) of the hybrid maximum likelihood/maximum a posteriori (ML/MAP) estimate. Therefore, this bound can be used as an asymptotic benchmark or metric of the radar performance in target measurement. Based on the HCRB, we also examine the effects of these nonideal factors on the target measurement performance by simulations. The results offer some perspective on the relationships between the system tolerances for these system errors and the radar parameters; these findings will be helpful in the system design of distributed MIMO radar.
      PubDate: THU, 09 NOV 2023 09:16:41 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Arithmetic Average Density Fusion-Part III: Heterogeneous Unlabeled and
           Labeled RFS Filter Fusion

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      Authors: Tiancheng Li;Ruibo Yan;Kai Da;Hongqi Fan;
      Pages: 1023 - 1034
      Abstract: This article, the third part of a series of papers on the arithmetic average density fusion approach and its application for target tracking, proposes the first heterogenous density fusion approach to scalable multisensor multitarget tracking where the interconnected sensors run different types of random finite set (RFS) filters according to their respective capacity and need. These diverse RFS filters result in heterogenous multitarget densities that are to be fused with each other in a proper means for more robust and accurate detection and localization of the targets. Our approach is based on Gaussian mixture implementations, where the local Gaussian components (L-GCs) are revised for probability hypothesis density (PHD) consensus, i.e., the corresponding unlabeled PHDs of each filter best fit their average regardless of the specific form of the local densities. To this end, a computationally efficient, coordinate descent approach is proposed which only revises the weights of the L-GCs, keeping the other parameters unchanged. In particular, the PHD filter, the unlabeled and labeled multi-Bernoulli (MB/LMB) filters are considered. Simulations have demonstrated the effectiveness of the proposed approach for both homogeneous and heterogenous fusion of the PHD-MB-LMB filters in different configurations.
      PubDate: MON, 20 NOV 2023 09:18:25 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Estimation of Atmospheric Radio Refraction Errors for Ground Stations

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      Authors: Seokkwon Kim;Sung-Wan Kim;
      Pages: 1035 - 1046
      Abstract: In communications between ground stations and vehicles, including launch vehicles and satellites, radio waves are bent owing to the vertical gradient of atmospheric refractivity. The refraction errors of the ground stations can be estimated based on the true positions of the vehicles. This study proposes algorithms to estimate the refractive elevation angle error by utilizing a bisection search based on ray tracing, where the lower and upper bounds are derived from the true elevation angle and range. The refractive range and altitude errors are determined accordingly, and the radio refractivity is modeled to decay exponentially with altitude from surface weather observations. As a case study, the radar estimation results are presented for the flight of a launch vehicle whose altitude ranges from the ground to 700 kilometers (km). At the radar station, the surface refractivity at sea level and scale height are computed from weather observations as 369.2 N-units and 6.07 km, respectively, showing differences from the reference values of 315 N-units and 7.35 km. In the latter part of tracking, the mean deviation between the estimated and measured altitudes using the proposed scheme is several meters, while those obtained from the reference refractivity model and formulas in previous studies are around 2 km. The estimation of refraction errors can be improved by the proposed method, even when the meteorological conditions of ground stations differ significantly from the reference atmosphere or the altitudes of vehicles are low. Moreover, refractive error processing on radar data is investigated in the case of the remote control of antenna systems for ground stations.
      PubDate: TUE, 28 NOV 2023 09:21:40 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Active Fault-Tolerant Strategy for Flight Vehicles: Transfer
           Learning-Based Fault Diagnosis and Fixed-Time Fault-Tolerant Control

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      Authors: Jiaxin Zhao;Pingli Lu;Changkun Du;Fangfei Cao;
      Pages: 1047 - 1059
      Abstract: In this article, we focus on the issue of active fault-tolerant strategy in the context of hypersonic vehicles. The proposed approach involves addressing the challenges of transfer learning-based fault diagnosis and implementing fixed-time fault-tolerant control. Based on a serial coupling of the 1-D residual convolution neural networks with attention mechanism (ResCNN-ATT) and the long short-term memory networks with attention mechanism (LSTM-ATT), a fault diagnosis deep residual convolution LSTM attention (ResCNN-LSTM-ATT) network is proposed. To deal with the insufficient data fault diagnosis problem, transfer learning technique is utilized based on the constructed ResCNN-LSTM-ATT network. Based on fault diagnosis information, a fixed-time nonsingular terminal sliding mode controller is designed to guarantee system tracking performance in the presence of actuator damage. Simulation results are performed to show the effectiveness of the proposed method based on the hypersonic vehicle model of NASA Langley Research Center.
      PubDate: FRI, 17 NOV 2023 09:19:49 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Blind Array Calibration of Mutual Coupling, Phase, and Gain for Automotive
           Radar

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      Authors: Solomon Goldgraber Casspi;Joseph Tabrikian;Hagit Messer;
      Pages: 1060 - 1073
      Abstract: One of the limiting factors in the performance of radar systems is the presence of mutual coupling (MC) between receive antenna elements or array imperfections, such as antenna phase and gain errors. Therefore, the data model is misspecified, resulting in high sidelobe levels in the beam pattern, low angular resolution, and biased angle estimation. In this article, we propose a blind calibration scheme for uniform planar arrays. Our method is based on multiple measurements of various scenarios, with an arbitrary and unknown number of targets-of-opportunity, unknown directions-of-arrival (DOAs), and unknown intensities. The proposed method is based on spatial smoothing and forward–backward averaging techniques, in order to identify the signal and noise subspaces. In the presence of MC or array imperfections, the signal subspace leaks into the noise subspace. The proposed method seeks to find and compensate for model misspecification using a model-order selection criterion. We evaluate the performance of our method through simulations, in terms of DOA estimation accuracy and resolution. Our results demonstrate that the DOA estimation performance after calibration with our proposed method is close to that of a perfectly calibrated array.
      PubDate: TUE, 28 NOV 2023 09:21:41 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Constrained Fixed-Time Terminal Sliding-Mode Control With Prescribed
           Performance for Space Manipulator System

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      Authors: Meiling Hu;Xuebo Yang;Hanlin Dong;
      Pages: 1074 - 1090
      Abstract: This article focuses on the high-precision position tracking problem for the free-float space manipulator (FFSM) system subject to parametric uncertainties, input saturation, and state constraints. To this end, the screw-based dynamics using the Newton–Euler iterative method, with a global and unique attitude representation, are established; then, a chattering-free fixed-time sliding-mode control (FTSMC) scheme is developed based on the proposed dynamics model. A radial basis function neural network (RBFNN) with proper hidden layers and nodes is integrated into the proposed controller, while modeling approximation errors are well-inhibited by adaptive laws. Since the tracking error has been explicitly constrained by an exponential decay function, the Lyapunov analysis indicates that the transient and steady performance and state constraints can be guaranteed simultaneously. Simulation experiments taking an FFSM with six-degree-of-freedom (DOF) illustrate the effectiveness of the proposed control scheme from two aspects: joint-space positioning and trajectory tracking.
      PubDate: TUE, 28 NOV 2023 09:21:41 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Efficient Long-Time Coherent Integration and Detection Algorithm for Radar
           High-Speed Target Based on the Azimuth Resampling Technology

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      Authors: Wenchao Yu;Xingyu Lu;Weimin Su;Hong Gu;Jianchao Yang;
      Pages: 1091 - 1101
      Abstract: Prolonging radar observation time is capable of enhancing noise robustness for weak target detection, whereas the range migration phenomenon induced by the target's high-speed movement will deteriorate the energy accumulation ability of the traditional moving target detection method. This article concerns long-time coherent integration for radar high-speed target and proposes an efficient detection algorithm based upon azimuth resampling. The proposed algorithm can be implemented via azimuth nonuniform fast Fourier transform and further developed into the sparse representation mechanism utilizing 2-D sparse Fourier transform. Furthermore, the dictionary dynamic updating strategy is presented to improve the weak target detectability in the multitarget observation environment. The processed results of both simulated and measured radar data have been displayed to validate the effectiveness of the presented algorithm.
      PubDate: MON, 20 NOV 2023 09:18:25 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Explicit Mathematical Formulation and Coverage Consistency of Satellite
           Constellation Designs for Repeating Ground Track Orbits

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      Authors: Soung Sub Lee;
      Pages: 1102 - 1112
      Abstract: To solve the coverage consistency and launch cost problems of continuous and periodic coverage, this study proposes a regular and symmetric constellation method for repeating ground track orbits and its closed-form solution for constellation design. The equations of motion developed in this study were implemented via a geometric analysis of Ptolemy's deferent and epicycle systems, and the solution was composed of design parameters. Therefore, unlike the existing constellation method, design and analysis can be realized simultaneously and efficiently. The performance of the proposed constellation method was compared with that of a typical Walker constellation in terms of the figure of merit sensitivity and coverage consistency, and its superior performance was verified. In addition, a comparison with Flower constellations using the same repeating ground track orbit was evaluated via launch cost problems and theoretical comparisons with respect to constellation design.
      PubDate: FRI, 17 NOV 2023 09:19:49 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Exponential Position and Attitude Tracking Control of Spacecraft With
           Unbiased Parameter Identification

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      Authors: Qin Zhao;Guang-Ren Duan;
      Pages: 1113 - 1128
      Abstract: This article investigates the position and attitude tracking control problem of a combined spacecraft subject to inertia parametric uncertainties, external disturbance, and input saturation in the postcapture of a noncooperative target. By introducing a group of auxiliary filters, a regression expression with respect to all the unknown parameters, including the mass, the inertia matrix, the center of mass location, and the disturbance-related parameters, is derived without requiring the linear and angular accelerations of the combined spacecraft. On this basis, a sufficient rank-condition of parameter identification is given based on concurrent learning. Subsequently, an adaptive tracking controller is designed for the combined spacecraft by incorporating concurrent learning into the backstepping technique. Within the framework of Lyapunov theory, the proposed controller can simultaneously guarantee the exponential convergence of position and attitude tracking and the unbiased parameter identification of all the uncertain parameters. Finally, numerical simulations are performed to illustrate the effectiveness of the proposed controller.
      PubDate: MON, 20 NOV 2023 09:18:25 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Joint Jam Perception and Adaptive Waveform Optimization for
           Anti-Interrupted Sampling Repeater Jamming

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      Authors: Song Wei;Yuyuan Fang;Yuxiao Song;Shaopeng Wei;Lei Zhang;
      Pages: 1129 - 1147
      Abstract: Recently, interrupted sampling repeater jamming (ISRJ) based on digital radio frequency memory (DRFM), which could easily form high-power false targets, has severely impacted radar detection capabilities. Conventional anti-ISRJ techniques ignore the operating characteristics of the jammer. Once the anti-ISRJ algorithm and the features of the jammer are mismatched, undesirable jamming residues or signal loss appear. In order to avoid these unwanted performance losses, an algorithm jointing the jam perception and the adaptive waveform is developed in this article. First, a bidirectional constant false alarm rate detector is introduced to locate and estimate the jamming component in radar echo. An ISRJ feature vector is then built to reconstruct and cancel jamming components for accurate detection under ISRJ. Second, an adaptive anti-ISRJ waveform is developed based on the vector, leading the DRFM to retransmit signals orthogonal to the real detection signal. Besides, the particle swarm optimization algorithm efficiently works out the optimal waveform parameters to improve the suppression performance. As radar switches to the adaptive anti-ISRJ waveform, long-term suppression of ISRJ is achieved without locating, estimating, and suppressing the ISRJ components in each echo. Simulated experiments demonstrate that the suggested algorithm can accurately detect radar targets under ISRJ.
      PubDate: WED, 22 NOV 2023 09:17:25 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Joint Multierror Calibration by Merging Errors in Distributed Coherent
           Aperture Radar Using Strong Scatter Echoes

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      Authors: Yuxuan Zhang;Jianxin Wu;Lei Zhang;
      Pages: 1148 - 1158
      Abstract: Full coherence needs strict phase synchronization in distributed coherent aperture radar (DCAR). However, DCAR suffers from various types of errors, including gain-phase, time alignment, and antenna position errors (APEs), which seriously damage phase synchronization. In addition, they are coupled and difficult to estimate individually. Thus, calibrating these coupled errors is still an intractable problem in improving coherence performance. In this article, we propose a joint multierror calibration method by merging errors using echoes of several inaccurate-position strong scatters. First, orthogonal waveforms are transmitted in DCAR to obtain transmitting degrees of freedom (DoF), and its echoes from strong scatters are investigated to estimate the coupled errors jointly. Second, parameter extraction is applied to acquire time delays (TD) and gain-phase of echo peaks as measurements in error estimation. In particular, gain-phase and time alignment errors are merged as equivalent gain-phase errors, avoiding the high precision requirement of time alignment errors. Then, a practical approximation of the maximum a posteriori (MAP) estimator and singular value decomposition (SVD) are employed to obtain estimation solutions for balancing efficiency and precision. Finally, the Cramér–Rao lower bounds (CRLB) of the required parameters and critical unknowns (e.g., antenna positions, radar positions, and attitude angles) are derived to analyze the estimation performance. Simulations are employed to validate the necessity of joint calibration, the advantages of merging errors, and the effectiveness of the proposed method.
      PubDate: THU, 30 NOV 2023 09:18:24 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Joint Target and User Assignment as well as Dwell Time and Spectrum
           Allocation in a Distributed Radar–Communication Coexistence Network

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      Authors: Haowei Zhang;Weijian Liu;Yuan Liu;Qun Zhang;Baobao Liu;
      Pages: 1159 - 1175
      Abstract: The optimal resource sharing is key to delivering the promise of radar–communication systems, since the spectrum is shared while competed to perform radar and communication tasks. In this article, a joint target and user assignment as well as dwell time and spectrum allocation strategy is proposed for the distributed radar–communication coexistence network. The optimization model is formulated as minimizing the sum of weighted position Bayesian Cramér–Rao lower bounds under the dwell time and spectrum budgets while meeting the communication downlink requirements. The optimization model is shown to be a mixed-integer programming problem, where the binary assignment variables and continuous resource allocation variables are coupled in both the objective function and constraints. A three-stage alternate optimization method (TSAOM) is developed for problem solving. The target assignment and dwell time allocation (TADTA) problem and the user assignment and spectrum allocation (UASA) problem are separately relaxed as convex ones. Then, a cyclical minimizer framework is applied for the suboptimal solution. Simulations confirm the tracking performance improvement of the proposed strategy compared with two baseline allocation strategies. The results also show the effectiveness and efficiency of the developed TSAOM in comparison with the baseline algorithm.
      PubDate: TUE, 28 NOV 2023 09:21:41 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Neural Network-Based Method for Orbit Uncertainty Propagation and
           Estimation

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      Authors: Xingyu Zhou;Dong Qiao;Xiangyu Li;
      Pages: 1176 - 1193
      Abstract: This article proposes a fast method for orbit uncertainty propagation and estimation. The proposed method is based on an orbit deviation propagation approach, which consists of an analytical two-body deviation propagation solution and a deep neural network (DNN) to compensate for the errors between the two-body and the true solutions. First, five types of sample forms for training the DNN are investigated, and the optimal one is selected through learning feature and training performance analyses. Then, an uncertainty propagation solution for propagating the mean and covariance is formulated by combining the DNN-based deviation propagation approach with an unscented transformation process. Finally, a more efficient version of the unscented Kalman filter (UKF) for orbit estimation is developed based on the formulated uncertainty propagation solution. The advantage of the proposed DNN-based method is that it avoids the integration of the state transition matrix or dozens of sigma points. The performance of the proposed method is investigated on a low-Earth-orbit example. Numerical results show that the proposed DNN-based estimation method can be one order of magnitude faster than the UKF and is comparable to the UKF in estimation accuracy. In addition, it estimates more accurately than the extended Kalman filter (EKF) and is approximately 10% faster than the EKF.
      PubDate: MON, 13 NOV 2023 09:18:16 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Power Allocation for Radar Tracking With LPI Constraint and Suppressive
           Jamming Threat

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      Authors: Longxiao Xu;Tianxian Zhang;Zhijie Ma;Yuanhang Wang;
      Pages: 1194 - 1207
      Abstract: In the electronic countermeasures environment, efficient power allocation is the key to guaranteeing the radar tracking accuracy, an unreasonable power may result in the radar transmit pulse being intercepted by the electronic reconnaissance receiver (ERR). As the number of intercepted pulses increases, the radar transmit signal would be identified and the radar would be jammed by the suppressive jamming, thus, the tracking accuracy would be decreased sharply. Therefore, in this article, to suppress the jamming threat, the power allocation problem for radar tracking under low probability of intercept (LPI) constraint is investigated. However, radar and ERR are noncooperative, the parameters of the ERR are considered unavailable, resulting in the unavailable explicit analytical expression of this power allocation problem. We focus on the effect of suppressive jamming on the radar received echo pulse queue and propose an interacting multiple power (IMP) based power allocation algorithm. Specifically, different lengths of echo pulse queues are processed in parallel, and different filtering results could be obtained by using the probability data association filters. These filtering results could be used as the feedback to update the parameters of the IMP-based algorithm, thus, dynamically adjust the radar transmit power at the next tracking frame. Numerical simulation results are presented to demonstrate that, in the electronic countermeasures environment, the tracking accuracy could be improved and efficient LPI performance could be maintained by consuming less power with the proposed IMP-based power allocation algorithm.
      PubDate: TUE, 28 NOV 2023 09:21:40 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Predicting Hypersonic Glide Vehicle Behavior With Stochastic Grammars

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      Authors: Emily R. Bartusiak;Michael A. Jacobs;Moses W. Chan;Mary L. Comer;Edward J. Delp;
      Pages: 1208 - 1223
      Abstract: Hypersonic glide vehicles are a new class of vehicles that fly at hypersonic speeds and have high maneuverability. These fast-moving targets exhibit different flight characteristics compared to conventional vehicles, so traditional tracking and defense systems require new methods to contend with them. In this article, we propose a machine learning method for predicting the behavior of hypersonic glide vehicles. Our method is based on a stochastic grammar, which is a mathematical framework that describes the possible transition patterns of sequences. We use the stochastic grammar to predict the transition patterns in hypersonic glide vehicle trajectories. Given a partial trajectory, our method uses the grammar to predict the hypersonic glide vehicle's future kinematics, such as its altitude, velocity, and acceleration. We evaluate our method on two datasets of simulated hypersonic glide vehicle trajectories and show that it can successfully predict hypersonic glide vehicle behavior, even in the presence of noise. We also show that our method can predict several minutes into the future and can accurately predict future hypersonic glide vehicle behavior based on shorter observation times. Our results suggest that our method has the potential to be a valuable tool for predicting the behavior of hypersonic glide vehicles.
      PubDate: WED, 29 NOV 2023 09:21:44 -04
      Issue No: Vol. 60, No. 1 (2023)
       
  • Spiking Neural Networks for Detecting Satellite Internet of Things Signals

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      Authors: Kosta Dakic;Bassel Al Homssi;Sumeet Walia;Akram Al-Hourani;
      Pages: 1224 - 1238
      Abstract: With the rapid growth of Internet of Things (IoT) networks, ubiquitous coverage is becoming increasingly necessary. Low earth orbit (LEO) satellite constellations for the IoT have been proposed to provide coverage to regions where terrestrial systems cannot. However, LEO constellations for uplink communications are severely limited by the high density of user devices, which causes a high level of cochannel interference. This research presents a framework that utilizes spiking neural networks (SNNs) to detect IoT signals in the presence of uplink interference. The key advantage of SNNs is the extremely low power consumption relative to traditional deep learning (DL) networks. The performance of the spiking-based neural network detectors is compared against state-of-the-art DL networks and the conventional matched filter detector. Results indicate that both DL and SNN-based receivers surpass the matched filter detector in interference-heavy scenarios, due to their capacity to effectively distinguish target signals amid cochannel interference. Moreover, our work highlights the ultralow power consumption of SNNs compared to other DL methods for signal detection. The strong detection performance and low power consumption of SNNs make them particularly suitable for onboard signal detection in IoT LEO satellites, especially in high interference conditions.
      PubDate: MON, 20 NOV 2023 09:18:25 -04
      Issue No: Vol. 60, No. 1 (2023)
       
 
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  Subjects -> ELECTRONICS (Total: 207 journals)
Showing 1 - 200 of 277 Journals sorted by number of followers
IEEE Transactions on Aerospace and Electronic Systems     Hybrid Journal   (Followers: 335)
Control Systems     Hybrid Journal   (Followers: 289)
IEEE Transactions on Geoscience and Remote Sensing     Hybrid Journal   (Followers: 235)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 213)
Electronics     Open Access   (Followers: 177)
Advances in Electronics     Open Access   (Followers: 171)
Electronics For You     Partially Free   (Followers: 159)
Electronic Design     Partially Free   (Followers: 156)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 145)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 94)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 92)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 88)
IEEE Transactions on Industrial Electronics     Hybrid Journal   (Followers: 88)
IEEE Transactions on Software Engineering     Hybrid Journal   (Followers: 87)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 81)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 70)
IET Power Electronics     Open Access   (Followers: 69)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 66)
IEEE Embedded Systems Letters     Hybrid Journal   (Followers: 59)
IEEE Transactions on Industry Applications     Hybrid Journal   (Followers: 58)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 57)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 55)
Advances in Power Electronics     Open Access   (Followers: 50)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 46)
IEEE Nanotechnology Magazine     Hybrid Journal   (Followers: 44)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 42)
IEEE Transactions on Biomedical Engineering     Hybrid Journal   (Followers: 36)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 34)
IET Microwaves, Antennas & Propagation     Open Access   (Followers: 33)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 32)
Electronics Letters     Open Access   (Followers: 32)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 32)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 30)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 28)
Microelectronics and Solid State Electronics     Open Access   (Followers: 27)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 25)
Journal of Sensors     Open Access   (Followers: 23)
International Journal of Power Electronics     Hybrid Journal   (Followers: 23)
IEEE Reviews in Biomedical Engineering     Hybrid Journal   (Followers: 21)
IEEE/OSA Journal of Optical Communications and Networking     Hybrid Journal   (Followers: 20)
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 19)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 18)
IET Wireless Sensor Systems     Open Access   (Followers: 18)
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 17)
Journal of Artificial Intelligence     Open Access   (Followers: 16)
Circuits and Systems     Open Access   (Followers: 16)
Archives of Electrical Engineering     Open Access   (Followers: 16)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 16)
International Journal of Control     Hybrid Journal   (Followers: 14)
Advances in Microelectronic Engineering     Open Access   (Followers: 14)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 14)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 13)
IETE Journal of Research     Open Access   (Followers: 13)
IEEE Solid-State Circuits Magazine     Hybrid Journal   (Followers: 13)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 13)
Nature Electronics     Hybrid Journal   (Followers: 13)
Machine Learning with Applications     Full-text available via subscription   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 12)
IEEE Transactions on Broadcasting     Hybrid Journal   (Followers: 12)
IEEE Transactions on Learning Technologies     Full-text available via subscription   (Followers: 12)
IEEE Women in Engineering Magazine     Hybrid Journal   (Followers: 12)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 12)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 12)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 11)
Frontiers in Electronics     Open Access   (Followers: 11)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 10)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Batteries     Open Access   (Followers: 10)
Superconductivity     Full-text available via subscription   (Followers: 10)
IETE Technical Review     Open Access   (Followers: 9)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 9)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 9)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 9)
ACS Applied Electronic Materials     Open Access   (Followers: 9)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 8)
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 8)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 8)
Journal of Signal and Information Processing     Open Access   (Followers: 8)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 8)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 8)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 8)
International Journal of Antennas and Propagation     Open Access   (Followers: 7)
Annals of Telecommunications     Hybrid Journal   (Followers: 7)
IEEE Transactions on Autonomous Mental Development     Hybrid Journal   (Followers: 7)
IEEE Magnetics Letters     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
Open Journal of Antennas and Propagation     Open Access   (Followers: 7)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 7)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 7)
Energy Storage Materials     Full-text available via subscription   (Followers: 7)
Chinese Journal of Electronics     Open Access   (Followers: 7)
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access   (Followers: 7)
Electronic Markets     Hybrid Journal   (Followers: 6)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 6)
International Journal of Electronics     Hybrid Journal   (Followers: 6)
Journal of Electronics (China)     Hybrid Journal   (Followers: 6)
Journal of Field Robotics     Hybrid Journal   (Followers: 6)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 6)
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 6)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal   (Followers: 6)
EPE Journal : European Power Electronics and Drives     Hybrid Journal   (Followers: 6)
Advanced Materials Technologies     Hybrid Journal   (Followers: 6)
Nanotechnology, Science and Applications     Open Access   (Followers: 5)
IEEE Transactions on Services Computing     Hybrid Journal   (Followers: 5)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 5)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 5)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 5)
Metrology and Measurement Systems     Open Access   (Followers: 5)
IEEE Pulse     Hybrid Journal   (Followers: 5)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 5)
Journal of Optoelectronics Engineering     Open Access   (Followers: 5)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
Journal of Power Electronics     Hybrid Journal   (Followers: 5)
Sensors International     Open Access   (Followers: 5)
e-Prime : Advances in Electrical Engineering, Electronics and Energy     Open Access   (Followers: 5)
Materials Today Electronics     Open Access   (Followers: 5)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 4)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 4)
IEEE Transactions on Haptics     Hybrid Journal   (Followers: 4)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 4)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
IETE Journal of Education     Open Access   (Followers: 4)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 4)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
EPJ Quantum Technology     Open Access   (Followers: 4)
Wireless and Mobile Technologies     Open Access   (Followers: 4)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Informatik-Spektrum     Hybrid Journal   (Followers: 3)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 3)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 3)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal   (Followers: 3)
Power Electronic Devices and Components     Open Access   (Followers: 3)
Journal of Nuclear Cardiology     Hybrid Journal   (Followers: 2)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Journal of Semiconductors     Full-text available via subscription   (Followers: 2)
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 2)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 2)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 2)
Journal of Information and Telecommunication     Open Access   (Followers: 2)
Advancing Microelectronics     Hybrid Journal   (Followers: 2)
IET Smart Grid     Open Access   (Followers: 2)
IET Energy Systems Integration     Open Access   (Followers: 2)
Transactions on Electrical and Electronic Materials     Hybrid Journal   (Followers: 2)
Energy Storage     Hybrid Journal   (Followers: 2)
IEEE Open Journal of the Industrial Electronics Society     Open Access   (Followers: 2)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
Journal of Electrical Bioimpedance     Open Access   (Followers: 1)
International Journal of High Speed Electronics and Systems     Hybrid Journal   (Followers: 1)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Електротехніка і Електромеханіка     Open Access   (Followers: 1)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 1)
Edu Elektrika Journal     Open Access   (Followers: 1)
Technical Report Electronics and Computer Engineering     Open Access   (Followers: 1)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 1)
Open Electrical & Electronic Engineering Journal     Open Access   (Followers: 1)
International Journal of Hybrid Intelligence     Hybrid Journal   (Followers: 1)
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 1)
Solid State Electronics Letters     Open Access   (Followers: 1)
Ural Radio Engineering Journal     Open Access   (Followers: 1)
IEEE Open Journal of Circuits and Systems     Open Access   (Followers: 1)
IEEE Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 1)
Elektronika ir Elektortechnika     Open Access   (Followers: 1)
Automatika : Journal for Control, Measurement, Electronics, Computing and Communications     Open Access   (Followers: 1)
npj Flexible Electronics     Open Access  
Transactions on Cryptographic Hardware and Embedded Systems     Open Access  
Emitor : Jurnal Teknik Elektro     Open Access  
IEEE Solid-State Circuits Letters     Hybrid Journal  
IEEE Open Journal of Industry Applications     Open Access  
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal  
Journal of Electronic Science and Technology     Open Access  
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Journal of Engineered Fibers and Fabrics     Open Access  
Jurnal Teknologi Elektro     Open Access  
IET Nanodielectrics     Open Access  
Elkha : Jurnal Teknik Elektro     Open Access  
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Jurnal Teknik Elektro     Open Access  
IACR Transactions on Symmetric Cryptology     Open Access  
Acta Electronica Malaysia     Open Access  
Bioelectronics in Medicine     Hybrid Journal  
Problemy Peredachi Informatsii     Full-text available via subscription  
Jurnal Rekayasa Elektrika     Open Access  
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Visión Electrónica : algo más que un estado sólido     Open Access  
Telematique     Open Access  
International Journal of Nanoscience     Hybrid Journal  
Semiconductors and Semimetals     Full-text available via subscription  

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Email: journaltocs@hw.ac.uk
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