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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Yuan, Wenjiang Zhang, Xian Sha, Lin Tiangong University Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tianjin300387 China Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment Tianjin300130 China Hebei University of Technology Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Tianjin300130 China

ISBN: (纸本)9798350338362

The wireless power transfer technology provides a feasible solution for scenarios where there is a demand for rotating power transfer. However, the rotating power transfer system is located in a small space and the design of each part is too compact, and when it is too close to the surrounding metal parts, the influence of the metal will cause the system parameters to change and reduce the energy transfer efficiency, and the eddy current loss generated in the metal will lead to temperature rise and affect the safe operation of the system. In this paper, we take an electrically excited motor as the application scenario, establish the equivalent influence model of the system lateral to the metal of different spacing and material, conduct circuit parameters and magnetic field analysis, and propose a toroidal DD structured coil for the design of wireless power transfer system according to the need to suppress the influence of metal and anti-rotation *** on the finite element method, a simulation model is established to analyze the transmission performance and the surrounding magnetic field of the toroidal DD coil under metal disturbance and to compare the metal influence and rotational offset resistance of the wireless power transfer Finally, an experimental platform of the rotating wireless power transfer system with a toroidal DD coil under the influence of metal at the end of the simulated motor is built to experimentally verify the theoretical and simulation models. The experimental results show that the proposed toroidal DD coil structure has better resistance to metal influence and rotational offset than other power transfer coil structures. © 2023 IEEE.

关键词： Energy transfer

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A Hybrid CNN-RF Classifier with Multi-Dimensional Early-Exit Strategy for Radio Frequency Fingerprinting

A Hybrid CNN-RF Classifier with Multi-Dimensional Early-Exit...

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IEEE International Conference on Communications (ICC)

作者： Zhongyi Wen Jiayan Gan Zhixing Du Qiang Li Ye Pan Huaizong Shao University of Electronic Science and Technology of China Chengdu China Nanhu Laboratory Jiaxing China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

With the development of wireless communication technology and the increasingly complex electromagnetic environment, radio frequency fingerprinting (RFF) plays a vital role in improving the security of communication and information systems. Recently, many RFF algorithms based on machine learning have emerged. However, most of them focus on improving the accuracy of RFF identification but ignore the computational cost. This paper proposes a novel classifier composed of a convolutional neural network (CNN) backbone with two random forest branches, called hybrid CNN-RF. Under the scheduling of the proposed multi-dimensional early exit strategy, hybrid CNN-RF can end early when processing “easy” samples to reduce the computational cost, and activate the inference of the subsequent network layer when processing “hard” samples to ensure accuracy. Finally, our experiments show that the proposed algorithm can reduce the computational cost by a factor of 3.41 while improving the accuracy by 1.5%.

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A Range Alignment Method Using Deep Learning for MMW ISAR Imaging

A Range Alignment Method Using Deep Learning for MMW ISAR Im...

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Frontiers of Electronic, Electrical and Information Engineering (ICFEEIE), International Conference on the

作者： Chaofan Zhao Chunguang Ma Xu Chen Y ong Luoab School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan China School of Electronic Science and Engineering UESTC Chengdu Sichuan China State Key Laboratory of Electromagnetic Space Cognition and Intelligent Control Technology Beijing China

ISBN: (数字)9798331528300

ISBN: (纸本)9798331528317

Range alignment is an important step in the imaging process of inverse synthetic aperture radar (ISAR) radar. In this paper, a novel deep learning network model is proposed to effectively tackle the challenge of distance alignment in millimeter-wave (MMW) ISAR imaging. The proposed model leverages a multi-layer bidirectional long short-term memory (bi-LSTM) network architecture to learn intricate features among echoes, thus facilitating precise alignment along the range direction. Meanwhile transfer learning was used to enhance the adaptability of the model to multiple signal-to-noise ratios (SNR). Experiment results show that the aligned images generated by proposed model in this paper are closer to the reference images in low SNR environments compared to traditional methods.

关键词： Deep learning Adaptation models Simulation Transfer learning Imaging Radar imaging Network architecture Millimeter wave radar Inverse synthetic aperture radar Signal to noise ratio

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Community Search Based on Containment control of Multi-Agent System with Opinion Leaders

Community Search Based on Containment Control of Multi-Agent...

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第43届中国控制会议

作者： Siyang Liu Tong Li Jinliang Shao Wei Xing Zheng School of Automation Engineering University of Electronic Science and Technology of China Laboratory of Electromagnetic Space Cognition and Intelligent Control School of Computer Data and Mathematical SciencesWestern Sydney University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Community search is the problem of identifying the community in which a given node *** from traditional community search methods based on specific topological structures,this paper proposes a community search algorithm based on containment control of multi-agent system by studying the influence of opinion leaders with competitive relationships in community ***,the Breadth-First Search method is used to locally sample the ***,containment control of multi-agent system is applied to obtain the final state of each node for local community ***,grounded in practical considerations,an effective leadership transfer mechanism is proposed to update the results and enhance search *** experiments on real-world networks and synthetic networks in comparison with three local community detection algorithms,the effectiveness and rationality of our algorithm are demonstrated.

关键词： Community Search Opinion Leaders Multi-Agent System Containment control

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Cognitive Jammer Time Resource Scheduling With Imperfect Information Via Fuzzy Q-Learning

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Gan, Linchuan Xiong, Kui Liao, Maosen Yu, Xianxiang Cui, Guolong University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Technology Beijing100089 China UESTC Shenzhen Institute for Advanced Study Shenzhen518038 China

Effective strategy generation of the jammer with inaccurate or undetermined information for combating the radar system is a challenging problem and the relevant research is scarce in existing work. This paper aims at exploring this promising topic and proposing a transmitting time slots scheduling strategy generation method for the cognitive jammer with imperfect time-matching information based on fuzzy reinforcement learning. First, the interaction between the jammer and environment is modeled as a Markov decision process (MDP) with uncertain state and continuous action, in which the goal of the jammer in interaction is to maximize accumulated discount pulse transmitting time slot jamming probability. Then, the uncertain pulse repeat intervals (PRIs) of the radar signal is inferred using the fuzzy inference system (FIS), where the inferred reward is evaluated by the correntropy of the inferred radar PRI and the previously measured one, thus presenting the fuzzy Q-learning based time scheduling (TSFQL) algorithm. Finally, numerical simulations in typical scenarios are performed to illustrate the effectiveness and superiority of the TSFQL algorithm over conventional methods. © 1965-2011 IEEE.

关键词： Markov processes

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UAV Detection with the Variance of Higher-Order Cumulants

UAV Detection with the Variance of Higher-Order Cumulants

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IEEE Conference on Wireless Communications and Networking

作者： Nanzhou Hu Jian Yang Wensheng Pan Qiang Xu Shihai Shao Youxi Tang National Key Laborary of Wireless Communications University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Due to the popularity of unmanned aerial vehicles (UAVs), UAV detection technology has attracted considerable attention. In order to widely deploy UAV detection for preventing civil UAVs, a UAV detection method that can be used with any transceiver and operate in parallel with any communication system is proposed in this paper. UAV flights influence electromagnetic environments during signal propagation, and the proposed method detects UAVs based on the variance of higher-order cumulants. In this paper, the principle of the method is deduced and proven, and the detection performance is analysed. Explicit expressions for the detection and false alarm probabilities are obtained, and the influencing factors are investigated. Finally, the feasibility of the proposed method is verified experimentally, and the detection distance, signal-to-noise ratio and detection height are analysed. The results give the effective detection distance and show that the proposed method has superior anti-noise performance.

关键词： Analytical models Communication systems Autonomous aerial vehicles Transceivers electromagnetics Signal to noise ratio

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Infrared Fuzzy Video Data Cleaning Based on Improved YOLOv5 11th

Infrared Fuzzy Video Data Cleaning Based on Improved YOLOv5

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11th China Conference on Command and control, C2 2023

作者： Gong, Hua Tian, Haoyuan Chen, Congang Hu, Yutong School of Science Shenyang Ligong University Shenyang110159 China Liaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry Shenyang110159 China Faculty of Science and Technology Beijing Normal University—Hong Kong Baptist University United International College Zhuhai519087 China National Key Laboratory of Electromagnetic Space Security Tianjing300308 China

ISBN: (纸本)9789819990207

Aiming at the problem that infrared fuzzy video data affect video quality, an improved YOLO v5 deep network model is established to carry out intelligent and accurate infrared fuzzy video data cleaning. Considering the problem of missing infrared fuzzy video data, a dataset named SYLU-INFRARED VIDEO DATASET is constructed. This dataset includes 5000 aerial and ground background infrared fuzzy video frames. Considering the spatiotemporal joint features of video data, a CBAM attention mechanism with two-dimensional attention allocation strategies is applied. This attention can automatically allocate the importance of video features extracted by YOLOv5 backbone in both spatial and channel dimensions. The experimental results show that the fusion model of CBAM and YOLO v5 enhances the cleaning effectiveness. CBAM-YOLOv5 has an increase of 17.8%, 28.4%, and 26.5% in Precision, Recall, and mAP compared to YOLOv7, respectively. © Chinese Institute of Command and control 2024.

关键词： Antennas

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Mitigating Receiver Impact on Radio Frequency Fingerprint Identification via Domain Adaptation

arXiv

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arXiv 2024年

作者： Yang, Liu Li, Qiang Ren, Xiaoyang Fang, Yi Wang, Shafei School of Information and Communication Engineering University of Electronic Science and Technology of China P. Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100083 China

Radio Frequency Fingerprint Identification (RFFI), which exploits non-ideal hardware-induced unique distortion resident in the transmit signals to identify an emitter, is emerging as a means to enhance the security of communication systems. Recently, machine learning has achieved great success in developing state-of-the-art RFFI models. However, few works consider cross-receiver RFFI problems, where the RFFI model is trained and deployed on different receivers. Due to altered receiver characteristics, direct deployment of RFFI model on a new receiver leads to significant performance degradation. To address this issue, we formulate the cross-receiver RFFI as a model adaptation problem, which adapts the trained model to unlabeled signals from a new receiver. We first develop a theoretical generalization error bound for the adaptation model. Motivated by the bound, we propose a novel method to solve the cross-receiver RFFI problem, which includes domain alignment and adaptive pseudo-labeling. The former aims at finding a feature space where both domains exhibit similar distributions, effectively reducing the domain discrepancy. Meanwhile, the latter employs a dynamic pseudo-labeling scheme to implicitly transfer the label information from the labeled receiver to the new receiver. Experimental results indicate that the proposed method can effectively mitigate the receiver impact and improve the cross-receiver RFFI performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Palmprint recognition

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state Feedback control of Distributed radar system with Packet dropouts and Time-varying delays

State Feedback Control of Distributed radar system with Pack...

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International Conference on Vacuum Electronics

作者： Xiaoxue Li Yunjiao Zhang Jing Yang Qiaoyu Li Xu Chen School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

In this paper, we present state feedback control for the distributed radar with packet dropouts and time-varying delays. The delays are time-varying. The packet dropouts mean the loss of the state measurement data. Firstly, the state feedback control design is transformed into stability analysis for a 2D stochastic system. Secondly, we obtain the stability condition based on linear matrix inequality (LMI) of mean-square asymptotic stability of the 2D stochastic system. And we give the method to get the law for the state feedback control design. The proposed method can stabilize the distributed radar with packet dropouts and time-varying delays. Finally, a numerical example is to show the validity of the methods.

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Virtual electromagnetic environment modeling based data augmentation for drone signal identification

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Journal of Information and Intelligence 2023年第4期1卷 308-320页

作者： Hanshuo Zhang Tao Li Yongzhao Li Zhijin Wen School of Telecommunications Engineering Xidian UniversityXi'an 710071China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing 100191China Guangzhou Institute of Technology Xidian UniversityGuangzhou 510000China

Radio frequency(RF)-based drone identification technologies have the advantages of long effective distances and low environmental dependence,which has become indispensable for drone surveillance ***,since drones operate in unlicensed frequency bands,a large number of co-frequency devices exist in these bands,which brings a great challenge to traditional signal identification *** learning techniques provide a new approach to complete endto-end signal identification by directly learning the distribution of RF *** such scenarios,due to the complexity and high dynamics of the electromagnetic environments,a massive amount of data that can reflect the various propagation conditions of drone signals is necessary for a robust neural network(NN)for identifying *** reality,signal acquisition and labeling that meet the above requirements are too costly to ***,we propose a virtual electromagnetic environment modeling based data augmentation(DA)method to improve the diversity of drone signal *** DA method focuses on simulating the spectrograms of drone signals transmitted in real-world environments and randomly generates extra training data in each training ***,considering the limited processing capability of RF receivers,we modify the original YOLOv5s model to a more lightweight *** losing the identification performance,more hardware-friendly designs are applied and the number of parameters decreases about *** performance evaluation,we utilized a universal software radio peripheral(USRP)X310 platform to collect RF signals of four drones in an anechoic chamber and a practical wireless *** results reveal that the NN trained with augmented data performs as well as that trained with practical data in the complex electromagnetic environment.

关键词： Drone signal identification Data augmentation Virtual electromagnetic environment modeling You Only Look Once Spectrogram

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