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2024 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2024

作者： Banerjee, Hemanga Ilioudis, Christos Clemente, Carmine Williams, Chris University of Strathclyde Centre for Signal and Image Processing Glasgow United Kingdom Defence Science and Technology Laboratory Porton Down Wiltshire United Kingdom

ISBN: (纸本)9798331505585

The paper introduces a novel model of a fully adaptive cognitive radar jamming approach, designed for the purpose of countering modern cognitive radars. Cognitive radar systems have been widely used in modern electromagnetic warfare and also serve as an effective counter to traditional jamming techniques. Cognitive jamming makes use of machine learning and adaptive signal processing strategies to dynamically adapt jamming strategies in response to real-time changes in target radar transmissions. The proposed model may be used to incorporate advanced reinforcement learning algorithms, signal feature extraction techniques, and waveform optimization techniques to create a cohesive system that can optimize jamming strategies with minimal prior knowledge of the electromagnetic environment. The paper also discusses the state-of-the-art in cognitive radar jamming technologies, and their efficiency in the face of intelligent radar equipped with advanced anti-jamming measures. Furthermore, it addresses current challenges and future directions in the field with considerations for defense and security applications. © 2024 IEEE.

关键词： Jamming

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Dynamic Emergency Vehicle Prioritization in a Connected Urban Environment: A Rule-Based Approach 8

Dynamic Emergency Vehicle Prioritization in a Connected Urba...

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8th International Conference on Intelligent Traffic and Transportation, ICITT 2024 - incorporating the 8th International Conference on Communication and Network technology, ICCNT 2024

作者： Adnane, Ayoub Harkat, Yacine University of Science and Technology Houari Boumediene USTHB Speech Communication and Signal Processing Laboratory LCPTS Faculty of Elecrtrical engineering Beb Ezzouar Algeria

ISBN: (纸本)9781643685601

Efficient and rapid management of Emergency Vehicles (EVs) such as ambulances, fire trucks, and police vehicles is crucial for saving lives and protecting property. Road intersections often become bottlenecks, hindering the swift movement of EVs despite their priority. This paper proposes an Emergency Vehicle Priority Management (EVPM) system leveraging IoV, 5G connectivity, GPS, and V2X communication. The system employs a rule-based algorithm and real-time data to dynamically adjust traffic signals and vehicle lanes, minimizing disruptions to general traffic while prioritizing EVs. Simulations conducted using the SUMO traffic simulator and real-world traffic data from TAPASCologne demonstrate significant improvements in travel times, waiting times, and overall traffic flow efficiency. Specifically, the EVPM system achieved reductions in average travel times by 367s, waiting times by 31s/km, waiting counts by 9, and time loss by 40s/km, which correspond to an improvement of 27.18%, 60.91%, 59.42%, and 53.54% respectively. These results highlight the potential of the proposed system to enhance urban traffic management and emergency response efficiency. © 2024 The Authors.

关键词： Travel time

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A Novel Postprocessing Method for Ship Detection in SAR Images Using Greedy Strategies 11

A Novel Postprocessing Method for Ship Detection in SAR Imag...

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11th Joint International Information technology and Artificial Intelligence Conference, ITAIC 2023

作者： Liu, Yunfeng Car, Tianyi Wu, Xiaonan Gu, Tong Zhang, Linglong Li, Yachao HangZhou Institute of Technology Xian University HangZhou China Institute of the Second Academy of Aerospace Science and Industry Beijing China Xian University National Key Laboratory of Radar Signal Processing Xian China

ISBN: (纸本)9798350333664

This article proposes a novel post-processing method for ship detection in synthetic aperture radar (SAR) images, which addresses the issue of head to tail separation that cannot be avoided when using morphological compensation to process candidate regions generated by the constant false alarm detection (CFAR). This algorithm first constructs a connected domain for the candidate regions generated by CFAR, uses a greedy strategy to update the centroid and size of the connected domain, and finally unreasonable candidate connected domains are removed to eliminate high noise sea clutter and large volume non ship targets. Compared to the morphological compensation post-processing method, the greedy strategy the greedy strategy fully utilizes the prior knowledge of ship data in the post-processing of constant false alarm ship detection results, overcomes the disadvantage of complex morphological compensation methods that require fine-tuning parameters in post-processing, and completely solves the problem of head tail separation that cannot be completely solved, improving the accuracy of synthetic aperture radar ship detection. The effectiveness and robustness of our method have been demonstrated by the data from the domestically produced Gaofen 3 satellite and the data from the European Space Agency's TerraSAR-X satellite. © 2023 IEEE.

关键词： constant false alarm rate (CFAR) greedy strategies post processing ship detection

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Attention-based Dual-Branch Network for Micro-Expression Recognition with Global-Local Feature Fusion 18

Attention-based Dual-Branch Network for Micro-Expression Rec...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Qi, Yupeng Ibrayim, Mayire Hamdulla, Askar Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China

ISBN: (纸本)9798350364132

Micro-expression(ME) is an uncontrollable muscle movement that appears on the face when people try to hide or inhibit their real emotions, which has the problems of short duration, small movement amplitude and uneven distribution. In order to solve the problem of localization and asymmetry in the distribution of ME features, this paper proposes a new two-branch attention network to recognize MEs, which utilizes the attention mechanism to capture global and local ME features. The network is mainly divided into three parts: data preprocessing, ME feature learning and feature fusion classification. First, the data preprocessing first extracts the ME optical flow features and then divides them into four regions, which are used as inputs to the two-branch network respectively. Second, the two-branch network uses the Inception-MSFE global multi-scale feature extraction network incorporating the attention mechanism (CBAM) and the Swin Transformer-based local feature extraction network for feature learning, respectively. Finally, MEs are predicted by fusing global and local features of MEs. Experimental validation is carried out on three datasets, CASME II, SAMM, and SMIC, which proves that Acc, UAR, and UF1 are 0.797, 0.702, and 0.698 on the SAMM dataset, respectively;Acc, UAR, and UF1 are 0.734, 0.723, and 0.729 on SMIC, respectively;and on the CASME II dataset, Acc, UAR and UF1 are 0.865, 0.872, and 0.889, respectively, which are competitive with other state-of-the-art methods. © 2024 IEEE.

关键词： Feature extraction

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A SPECIAl TYPE OF THE PASSIVE radar IMAGING USING THE SELF-MULTIPATH signalS ON SATELLITES

A SPECIAl TYPE OF THE PASSIVE RADAR IMAGING USING THE SELF-M...

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IET International radar Conference 2023, IRC 2023

作者： Zhou, Yi Zhou, Ying Ma, Hui Zhou, Shenghua Zhang, Yujie Sun, Haoran Wang, Xue Liu, Lin Liu, Hongwei National Laboratory of Radar Signal Processing Xidian University Xi'an China Beijing Institute of Remote-sensing Equipment Science and Technology Millimeter-wave Laboratory Beijing China Institude of Information Sensing Xidian University Xi'an China

ISBN: (纸本)9781839539954

This paper investigates the feasibility of observing the satellite by its own transmitted signals. The system is a special type of the passive radar (PR) using the self-multipath signal (SMS) on satellites. Firstly, the geometry of the SMS-based PR (SMS-PR) is formulated and analysed, and then the direct signals containing self-multipath are modelled. Secondly, the in-pulse matched filtering and the direct-path cancellation are individually considered and corresponding processing algorithms are given. Thirdly, the process algorithms are proposed of the SMS-PR imaging of the satellite, based on the inverse synthetic aperture radar (ISAR) and the orthogonal matching pursuit (OMP). Finally is the post-processing of the proposed scheme is verified by the simulation results. In addition, the paper analyses also the conditions guaranteeing the successful imaging. © The Institution of Engineering & technology 2023.

关键词： radar imaging

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Misspecified CRB for TDOA based Passive Localization 16

Misspecified CRB for TDOA based Passive Localization

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16th International Conference on signal processing Systems, ICSPS 2024

作者： Cao, Jingmin Wan, Qun Chen, Ying Du, Fen Liu, Yang Li, Wei University of Electronic Science and Technology of China Chengdu China Southwest China Institute of Electronic Technology Chengdu China National Key Laboratory on Blind Signal Processing Chengdu China

ISBN: (纸本)9781510689251

Misspecified CRB (MCRB) is a lower bound on the variance of estimators when the parametric mode is not fully characterizing the behavior of the measurements. This paper derives the TDOA localization model and the performance bound under misspecification of the mean value of the measurement noise. The MCRB can not only play the same role as the CRB but also be used to assess performance under misspecified model. It’s a generalization of the classical CRB to misspecified model framework. For comparison, the specified CRB for TDOA localization with non-zero-mean Gaussian model is aslo investigaed. MCRB is equate to the specified CRB when the mean value of the measurement noise is equal to zero. The simmulation results are satisfy the theoretical analysis. © 2025 SPIE.

关键词： Velocity measurement

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A Range-Ambiguous Clutter Suppression Method via EPC-MIMO Bistatic Airborne radar 5

A Range-Ambiguous Clutter Suppression Method via EPC-MIMO Bi...

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5th International Conference on Information Communication and signal processing, ICICSP 2022

作者： Wang, Yi Zhu, Shengqi He, Xiongpeng Li, Ximin Wu, Xiaochun Guo, Yifan Xidian University National Laboratory of Radar Signal Processing Xi'an China School of Marine Science and Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781665485890

In this paper, the multiple-input multiple-output (MIMO) bistatic radar with element-pulse coding (EPC) is studied for suppressing range-ambiguous clutter. The EPC technology employs a series of weighting coefficients to modulate initial phase of each transmit pulse at each transmit element. Thus, it provides extra degrees of freedom in transmit spatial frequency domain. A novel framework of EPC-MIMO bistatic radar is first proposed to acquire transmit-receive-Doppler information of clutter spectrum. Moreover, clutter spectrum property of the established model is analyzed. Due to the coding dependence, a scheme of decoding procedure is given, and an appropriate compensating vector is constructed. After this, three-dimensional adaptive processing is adopted to suppress the separated range-ambiguous clutter. Simulation results are presented to verify the clutter spectrum characteristics in EPC-MIMO bistatic radar and to demonstrate its merits in range-ambiguous clutter suppression. © 2022 IEEE.

关键词： Clutter (information theory)

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A Least-Squares-Based Algorithm for Estimating ATI SAR Interferometric Phase Errors 2

A Least-Squares-Based Algorithm for Estimating ATI SAR Inter...

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2nd IEEE International Conference on signal, Information and Data processing, ICSIDP 2024

作者： Wang, Zhibin Guo, Huancheng Zhang, Qingjun Qi, Zhang Li, Zhengfang He, Changshan China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China Xidian University National Key Laboratory of Radar Signal Processing Xi'an China Beijing Institute of Technology School of Information and Electronics Beijing China

ISBN: (纸本)9798331515669

ATI SAR systems have immense potential for high-resolution, wide-swath measurements of sea surface currents. However, due to the sensitivity of ATI interferometric phase to phase errors, the retrieval of sea surface currents using ATI technology often relies on ground scene calibration. To address this issue, we first review the principles of ATI SAR sea surface current measurement and analyze the sources and characteristics of phase errors. Based on this analysis, we propose a least-squares-based method for estimating and compensating ATI SAR phase errors. This method ensures robust estimation and correction of phase errors by accounting for their distinct distribution characteristics. Finally, we validate the proposed method using data from the PIESAT-1 satellite constellation, demonstrating its effectiveness. © 2024 IEEE.

关键词： Ocean currents

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A Sparse Recovery Method on EPC-MIMO radar System 5

A Sparse Recovery Method on EPC-MIMO Radar System

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5th International Conference on Information Communication and signal processing, ICICSP 2022

作者： Liu, Kun He, Xiongpeng Liao, Guisheng Xu, Jingwei Zhu, Shengqi Guo, Yifan Xidian University National Laboratory of Radar Signal Processing Xi'an China School of Marine Science and Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781665485890

Multiple-Input Multiple-Output radar with Element-Pulse Coding (EPC) is a novel way to address the performance degradation caused by range ambiguity in space-time adaptive processing. In this paper, we use the sparse recovery method to solve the problem that EPC-MIMO has a large demand for independent and identically distributed (IID) samples. On the one hand, we use the Sparse Bayesian Learning (SBL) to achieve space-time spectral estimation under the small sample condition, and on the other hand, the use of prior knowledge, reduce the redundancy of the sparse recovery dictionary and improve the computational efficiency of the algorithm. The simulation results demonstrate the effectiveness of the proposed method. © 2022 IEEE.

关键词： MIMO systems

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A Lightweight UAV Visual Obstacle Avoidance Algorithm Based on Improved YOLOv8

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Computers, Materials & Continua 2024年第11期81卷 2607-2627页

作者： Zongdong Du Xuefeng Feng Feng Li Qinglong Xian Zhenhong Jia College of Computer Science and Technology Xinjiang UniversityUrumqi830046China Signal Detection and Processing Autonomous Region Key Laboratory Xinjiang UniversityUrumqi830046China Xinjiang Uygur Autonomous Region Research Institute of Measurement and Testing Urumqi830000China

The importance of unmanned aerial vehicle(UAV)obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance,thereby protecting people and *** propose UAD-YOLOv8,a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle *** algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2(DCNv2)to optimize the cross stage partial bottleneck with 2 convolutions and fusion(C2f)***,it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable convolution(UGDConv)series of lightweight convolutions and a lightweight detection *** on this,we designed a visual obstacle avoidance algorithm that can improve the obstacle avoidance performance of UAVs in different *** particular,we propose an adaptive distance detection algorithm based on obstacle attributes to solve the ranging problem for multiple types and irregular obstacles to further enhance the UAV’s obstacle avoidance *** verify the effectiveness of the algorithm,the UAV obstacle detection(UAD)dataset was *** experimental results show that UAD-YOLOv8 improves mAP50 by 3.4%and reduces GFLOPs by 34.5%compared to YOLOv8n while reducing the number of parameters by 77.4%and the model size by 73%.These improvements significantly enhance the UAV’s obstacle avoidance performance in complex environments,demonstrating its wide range of applications.

关键词： Unmanned aerial vehicle obstacle detection obstacle avoidance algorithm

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