Iet Radar Sonar and Navigation最新文献_第5页

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-03-29

引用次数: 0

Dynamic Multistatic Synthetic Aperture Radar Imaging: Resolving Motion via Differential Semblance and k-Space Diversity 动态多静态合成孔径雷达成像：通过差分相似度和k空间分集来分辨运动

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-03-23 DOI: 10.1049/rsn2.70107

Francis Watson, Joshua Hellier, Daniel Andre

{"title":"Dynamic Multistatic Synthetic Aperture Radar Imaging: Resolving Motion via Differential Semblance and k-Space Diversity","authors":"Francis Watson, Joshua Hellier, Daniel Andre","doi":"10.1049/rsn2.70107","DOIUrl":"https://doi.org/10.1049/rsn2.70107","url":null,"abstract":"Moving targets are well known to appear displaced and defocused in synthetic aperture radar (SAR) imagery, complicating both detection and recognition—especially when displaced into cluttered regions, where signal-to-background ratio is reduced. Multistatic SAR offers the potential to extract rich scene information, including enhanced moving target imaging, yet fully exploiting this diversity remains a significant challenge. We present a novel multistatic SAR collection geometry and processing framework that enables moving target indication, approximate signature extraction, motion estimation, and high-fidelity image formation. By minimising differences in refocused signatures across multistatic channels via differential semblance optimisation, we estimate target velocity and use it to form joint multichannel reconstructions which suppress background clutter and enhance target features. The proposed collection geometry allows flexibility to balance spatial resolution and diversity in observation angles with motion sensitivity, and maximises the information content of multistatic data for a desired deployment aim. Our methodology is validated using laboratory-collected SAR data, and is readily applicable to UAV-based systems, offering practical flexibility in deployment for scenarios requiring both fine-resolution imaging and robust moving target detection.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148012199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-03-23

引用次数: 0

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-03-19

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Controlled Laboratory Evaluation of the Evolving GNSS RFI Threat Space 不断发展的GNSS RFI威胁空间的受控实验室评估

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-03-19 DOI: 10.1049/rsn2.70136

Aiden J. Morrison, Nadezda Sokolova, A. Winter

{"title":"Controlled Laboratory Evaluation of the Evolving GNSS RFI Threat Space","authors":"Aiden J. Morrison, Nadezda Sokolova, A. Winter","doi":"10.1049/rsn2.70136","DOIUrl":"https://doi.org/10.1049/rsn2.70136","url":null,"abstract":"For reasons of both economic motivation and military conflicts, the intentional interference with critical global navigation satellite systems (GNSS) signals has evolved from nearly unheard of to a daily occurrence over the previous 20 years. As the interference threat is gradually evolving in coherence with the development of new mitigation strategies, it is important to monitor its evolution and keep the GNSS receiver testing capabilities in alignment. This article presents the motivations and outcomes of a 6 year process to first measure, characterise and understand the ecosystem of harmful radio frequency interference (RFI) signals, to development of a comprehensive evaluation framework incorporating software defined radio (SDR) based flexible signal generation. The developed simulation framework allows controlled production of both observed and theorised signal families over the entirety of their measured parameters spaces, and the efficient collection of observation data produced by an array of GNSS receivers to determine if given RFI modulations were stimulating enhanced failure modes in the devices under test. The test results discussed in this paper clearly show that consideration of the parameter space over which the RFI signal types occur must be taken into account when determining expected RFI impacts on GNSS receivers.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning to Optimise FISTA-PnP for Sparse Radar Imaging 稀疏雷达成像的FISTA-PnP优化学习

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-03-18 DOI: 10.1049/rsn2.70128

Yao Zhao, Yuliang Zheng, Kuoye Han, Ye Tian, Bingo Wing–Kuen Ling, Zhe Zhang, Xiaoshuai Pei, Zhihong Zhang

{"title":"Learning to Optimise FISTA-PnP for Sparse Radar Imaging","authors":"Yao Zhao, Yuliang Zheng, Kuoye Han, Ye Tian, Bingo Wing–Kuen Ling, Zhe Zhang, Xiaoshuai Pei, Zhihong Zhang","doi":"10.1049/rsn2.70128","DOIUrl":"https://doi.org/10.1049/rsn2.70128","url":null,"abstract":"This work introduces a plug-and-play (PnP) framework for sparse radar imaging that combines the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) with reinforcement learning (RL)-based parameter adaptation. Sparse radar imaging aims to reconstruct high-resolution images from undersampled measurements, but conventional PnP pipelines rely on manual parameter tuning, and ADMM-based solvers often converge slowly with high computational costs. To address these issues, the proposed framework leverages FISTA's momentum-accelerated updates for faster convergence, along with an RL policy that adaptively sets the step size, denoising strength and stopping criterion. By casting parameter selection as a sequential decision-making problem, we eliminate the need for handcrafted schedules. Extensive experiments on simulated datasets, including AIR-SARShip maritime targets, show consistent improvements over classical parameter-selection strategies and recent unrolled optimisation networks. At a 25% sampling ratio, the framework achieves high-fidelity reconstructions in just 10 iterations, compared to 20–30 iterations for competing methods. Overall, the RL-enhanced FISTA-PnP framework provides an efficient solution for high-quality radar image reconstruction from sparse measurements, with strong potential for real-world applications. Preliminary tests on real RADARSAT-1 echoes also show consistent improvements.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph-Based Spectrum Sensing via Quadratic Form of Adjacency Matrix 基于二次型邻接矩阵的图谱频谱感知

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-03-18 DOI: 10.1049/rsn2.70124

Yan Gao, Guobing Hu, Yunfei Chen

{"title":"Graph-Based Spectrum Sensing via Quadratic Form of Adjacency Matrix","authors":"Yan Gao, Guobing Hu, Yunfei Chen","doi":"10.1049/rsn2.70124","DOIUrl":"https://doi.org/10.1049/rsn2.70124","url":null,"abstract":"Conventional graph-based spectrum sensing algorithms often underperform in low signal-to-noise ratio (SNR) environments because they rely on the topological features of the graph transformed from the signals, thereby failing to sufficiently integrate the statistical characteristics of the original signal. To address this, we propose a novel algorithm that integrates graph topological and statistical features. First, the power spectrum of the observed signal after block summation (BS) is utilised as the input to the quantisation-based graph transformation, where normalised samples are clustered according to quantisation intervals, and their maximum values are assigned as the graph signal. The detection statistic is subsequently derived from the quadratic form of the graph signal, utilising the adjacency matrix of the graph, thereby enabling robust primary user detection. Furthermore, we approximate the probability density function (PDF) of the proposed statistic using the Gamma distribution based on the U-statistic and data exploration to enable the calculation of the threshold and detection probability. The simulation results indicate that the proposed algorithm outperforms existing non-convolutional neural network (non-CNN) spectrum sensing algorithms while maintaining moderate computational complexity.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online Moving Interference Target Perception and Suppression for Passive Radar Detection of UAVs 无人机被动雷达在线运动干扰目标感知与抑制

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-03-18 DOI: 10.1049/rsn2.70120

Tianrun Wang, Shengheng Liu, Yuan Feng, Yingshi Liu, Chenyu Wen, Zhi Wang, Zhenghang Wang, Tao Shan

{"title":"Online Moving Interference Target Perception and Suppression for Passive Radar Detection of UAVs","authors":"Tianrun Wang, Shengheng Liu, Yuan Feng, Yingshi Liu, Chenyu Wen, Zhi Wang, Zhenghang Wang, Tao Shan","doi":"10.1049/rsn2.70120","DOIUrl":"https://doi.org/10.1049/rsn2.70120","url":null,"abstract":"This paper addresses moving interference targets (MITs) in passive radar detection of low-altitude unmanned aerial vehicles (UAVs) by introducing a strategy that reinterprets strong scattering interference as target-like responses. An online perception of MIT-dominated areas is achieved through spatiotemporal statistical analysis and iterative processing of detection results. Targets within these identified areas are subsequently removed, effectively suppressing low-altitude MITs and reducing the false-alarm rate. A method for estimating the occurrence frequency of low-altitude interference targets based on a first-order autoregressive model is developed. This paper provides a detailed theoretical analysis of the weight-update mechanism and includes a proof of the algorithm's convergence. Furthermore, a dynamic-coefficient method and an adaptive-coefficient method are introduced to enhance convergence performance and enable adaptive adjustments to changes in the target-occurrence frequency. Simulation results demonstrate that the proposed algorithm converges within 100 iterations and achieves a postconvergence standard deviation of 0.0078. Field experiments show removal rates of 87.5% and 72.9% for low-altitude interference targets, with corresponding UAV false-removal rates of 20.26% and 4.17%, respectively.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-03-18

引用次数: 0

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-03-13

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