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Combining Local Electromagnetic Scattering and Global Structure Features for SAR Open Set Recognition

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12572-12587页

作者： Lin, Qinjie Sun, Hao Xu, Yanjie Wang, Junyu Ji, Kefeng Kuang, Gangyao National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology Changsha410073 China

Automatic target recognition (ATR) is crucial for synthetic aperture radar (SAR) image interpretation. However, existing SAR ATR primarily rely on algorithms from the field of computer vision, many methods have not adequately considered the imaging sensitivity of SAR images. Moreover, SAR ATR typically follows the closed set assumption, but in dynamic and open real-world environments, SAR systems may encounter target classes that have not been seen during training. To address these issues, this paper proposes a SAR open set recognition (OSR) method that combines local electromagnetic scattering and global structure features. The global structural features focus on the overall relationship between target and background, while the local electromagnetic scattering features can effectively capture the scattering characteristics of target. Hence, we construct a global and local feature guidance (GLGE) network, fuses these complementary features as prior knowledge to guide deep features. Then, a class-anchor-based loss function is constructed by Anchor loss and Tuple loss, which cluster the features of known classes targets around predefined class anchors. Finally, the distance ratio is calculated to identify known classes targets and reject unknown classes targets. The experimental results on the MSTAR and SAR-ACD datasets show that our method outperforms other OSR methods in recall, precision, F1, and accuracy, demonstrating a superior performance in OSR. © 2008-2012 IEEE.

关键词： Automatic target recognition

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Integrated online identification of aerodynamic and thrust parameters for air-breathing aircraft 3

Integrated online identification of aerodynamic and thrust p...

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2024 3rd International Conference on Aerospace and Control Engineering, ICoACE 2024

作者： Jiang, Zhenyu Li, Xiaodong Liu, Shuang College of Aerospace Science and Engineering National University of Defense Technology Hunan Changsha410073 China Hunan Provincial Key Laboratory of Aerospace Cross-Domain Flight Vehicle System and Control Technology Changsha410073 China

A method for integrated online identification of aerodynamic and thrust parameters has been proposed. Firstly, a motion model of an aircraft powered by a solid rocket ramjet engine was established in the longitudinal plane. Secondly, polynomial models of aerodynamic and thrust coefficients were established for the horizontal flight segment. Then, an integrated online identification simulation of aerodynamic and thrust parameters was carried out using the unscented Kalman filter method. The results indicate that the proposed method has high identification accuracy and efficiency. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Kalman filters

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Few-Shot Source Separation for IoT Anti-Jamming via Multi-Task Learning and Meta-Learning

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IEEE Internet of Things Journal 2025年第11期12卷 16761-16776页

作者： Guo, Pengcheng Yu, Miao Li, Cheng An, Kang Sun, Yifu Chatzinotas, Symeon Niyato, Dusit National University of Defense Technology Sixty-Third Research Institute China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System China Kyung Hee University College of Electronics and Information Yongin-si17104 Korea Republic of Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

Malicious jamming attacks pose a significant threat to the integrity and performance of Internet of Things (IoT) networks. However, many jamming patterns are rare or infrequent, which makes them difficult to counter effectively. This paper addresses the critical issue of anti-jamming (AJ) under few-shot sample conditions in IoT networks. Source separation is a key component of AJ communication. Although deep learning-based source separation has demonstrated significant advantages, it typically requires a large amount of labeled data, which can be impractical in certain environments. To overcome this challenge, we propose two novel schemes that leverage multi-task learning (MTL) and meta-learning (ML) to enhance the model's signal separation capabilities within the constraints of limited sample scenarios. MTL enhances robustness by leveraging shared representations across tasks, while ML allows for rapid adaptation to novel jamming signals with minimal samples. Specifically, we employ a modified separation model, SepFormer, as our baseline and integrate MTL and ML schemes to enable the separation of unknown or few-shot jamming signals. Additionally, we have constructed two datasets encompassing both simulated and real-world environmental data to test and evaluate the performance of the proposed methods. Simulation results demonstrate the superior AJ performance of our schemes, particularly when compared with a direct application of the separation model with few-shot samples. Furthermore, our evaluation of performance across various jamming scenarios and interference-to-signal ratios (ISR) further confirms the effectiveness of our proposed scheme. © 2014 IEEE.

关键词： Zero-shot learning

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Random Coefficient-Moving Product Based Wireless key Generation

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China Communications 2025年第2期22卷 199-212页

作者： Lu Xinjin Lei Jing Li Wei College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang 471003China China South Industries Group Corporation Zhi Yuan Institute310012China

The physical layer key generation technique provides an efficient method,which utilizes the natural dynamics of wireless ***,there are some extremely challenging security scenarios such as static or quasi-static environment,which lead to the low randomness of generated ***,the coefficients of the static channel may be dropped into the guard space and discarded by the quantization approach,which causes low key generation *** tackle these issues,we propose a random coefficient-moving product based wireless key generation scheme(RCMP-WKG),where new random resources with remarkable fluctuations can be obtained by applying random coefficient and by moving product on the legitimate ***,appropriate quantization approaches are used to increase the key generation ***,the security of our proposed scheme is evaluated by analyzing different attacks and the eavesdropper’s mean square error(MSE).The simulation results reveal that the proposed scheme can achieve better performances in key capacity,key inconsistency rate(KIR)and key generation rate(KGR)compared with the prior works in static ***,the proposed scheme can deteriorate the MSE performance of the eavesdropper and improve the key generation performance of legitimate nodes by controlling the length of the moving product.

关键词： moving product random coefficient static environment wireless key generation

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Enhancing Geolocation Accuracy of High-Altitude Airborne SAR Through Tropospheric Delay Compensation

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Xiang, Yaobing Sun, Yuli Lei, Lin Ji, Kefeng Kuang, Gangyao National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology Changsha410073 China

Geolocation is a crucial step in the processing of synthetic aperture radar (SAR) images. High-altitude airborne SAR systems present unique geolocation challenges due to travelling long distances through the troposphere. However, the impact of tropospheric delay on geolocation is often overlooked in existing airborne SAR studies, which can lead to inaccuracies. To address this issue, we propose a new positioning method, the tropospheric delay-compensated range-Doppler (TDC-RD) model. The TDC-RD model leverages reference atmospheric models to estimate and compensate for the tropospheric delay in SAR images. This model effectively mitigates the impact of tropospheric delay in SAR geolocation. To further optimize the TDC-RD model solution, a digital elevation model (DEM)-assisted dual iteration method is proposed. This method iteratively adjusts the target's plane position and elevation in an alternating manner. The effectiveness of the TDC-RD model has been validated through both simulation experiments and actual flight experiments. The results show a significant improvement in geolocation accuracy compared to existing methods, with a maximum reduction of 11.39 m and 24.33% in the mean absolute error (MAE) of SAR geolocation. The TDC-RD model has a great advantage in long-range SAR geolocation. Our research enhances the accuracy and stability of high-altitude airborne SAR geolocation without requiring ground control points. © 2025 IEEE.

关键词： Troposphere

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Theory and prediction of tropical cyclones and induced precipitation

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Atmospheric and Oceanic science Letters 2025年第2期18卷 1-2页

作者： Lili Lei Jianfang Fei Wen Zhou Key Laboratory of Mesoscale Severe Weather Ministry of Educationand School of Atmospheric SciencesNanjing UniversityNanjingChina College of Meteorology and Oceanography National University of Defense TechnologyChangshaChina Key Laboratory of Polar Atmosphere-Ocean-Ice System for Weather and Climate Ministry of EducationDepartment of Atmospheric and Oceanic Sciences&Institute of Atmospheric SciencesFudan UniversityShanghaiChina Key Laboratory for Polar Science of the MNR Polar Research Institute of ChinaShanghaiChina

The disasters caused by tropical cyclones(TCs),including gale-force winds,heavy rainfall,and storm surges,have profound social and economic impacts,which are closely associated with the track,intensity,and structure of *** the past few decades,significant progress has been made in developing theories and understanding the mechanisms of TC genesis and development,as well as advancing the monitoring and forecasting of TCs.

关键词： tropical prediction cyclones

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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Autonomous Traffic Prediction for LEO Satellite-Based IoT Based on Satellite Spatiotemporal Features Mapping

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IEEE Internet of Things Journal 2025年

作者： Gong, Lizeng Chen, Quan Yang, Lei Yin, Zhenglong Wang, Yi National University of Defense Technology State Key Laboratory of Space System Operation and Control College of Aerospace Science and Engineering Changsha410073 China Nanjing Research Institute of Electronics Technology Nanjing210013 China

The traffic prediction method for Low Earth Orbit (LEO) satellite-based Internet of Things (IoT) provides prior conditions for addressing challenges in resource allocation and management of LEO satellite-based IoT. However, traditional methods rely on the pre-generation of terrestrial centers, leading to substantial computational complexity and delays when applying prediction results to LEO satellite-based IoT. Therefore, achieving autonomous LEO satellite-based IoT traffic prediction is essential. Unlike the spatiotemporal features of terrestrial IoT, LEO satellite-based IoT traffic is influenced by both satellite dynamics and terrestrial user behavior. Fortunately, real IoT traffic information can be shared between adjacent satellites through Inter-satellite Links (ISL). Inspired by the above, this paper proposes, for the first time, an autonomous LEO satellite-based IoT traffic prediction method. A Spatiotemporal Fusion Neural Network (SFNN) employing a four-layer hybrid neural network architecture is designed to accommodate the unique spatiotemporal features of LEO satellite-based IoT traffic. The simulation results demonstrate that the proposed method has low computational complexity and achieves a better prediction performance than the five baseline methods in LEO constellations. Further simulations demonstrate that the proposed method achieves higher accuracy in large-scale LEO constellations, and the algorithm remains applicable under ISL instability and satellite failure conditions. © 2014 IEEE.

关键词： Resource allocation

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integration of SSPP Antenna and Vivaldi Arrays for 5G Sub-6 GHz and Millimeter-Wave Applications

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Cheng, Houyuan Li, Shangru Fu, Yang Yang, Helin Wu, Jiong Zhou, Xiangli Shi, Xintong Central China Normal University College of Physical Science and Technology Wuhan430079 China State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronics and Information System National University of Defense Technology China

An integrated antenna system covering 5G Sub-6 GHz and millimeter-wave bands is proposed, combining a spoof surface plasmon polariton (SSPP) antenna and two Vivaldi arrays. The integrated design comprises three radiators: an SSPP array, a linear tapered slot (LTS), and Vivaldi arrays. The SSPP antenna (excited by port 1) operates in the Sub-6 GHz band (SUB: 4–4.9 GHz and 5.3–6 GHz) through its SSPP array, while the LTS covers millimeter-wave band 1 (MMW1: 23–25.8 GHz). Dual Vivaldi arrays, symmetrically distributed on both sides of the antenna and excited by ports 2 and 3, operate in millimeter-wave band 2 (MMW2: 18–25.8 GHz). Measured results demonstrate average realized gains of 11 dBi (SUB), 13 dBi (MMW1), and 14 dBi (MMW2). This design shows potential for 5G communication and radar systems. © 2002-2011 IEEE.

关键词： Millimeter waves

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