Iet Radar Sonar and Navigation最新文献

Range-Free Localisation for Monostatic Passive Radar Employing Multi-Narrowband Illuminators 基于多窄带光源的单站无源雷达无距离定位

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-04-10 DOI: 10.1049/rsn2.70139

Chenggeng Zhao, Chuanbin Zang, Lianpeng Li, Heyue Huang, Xingpeng Mao

{"title":"Range-Free Localisation for Monostatic Passive Radar Employing Multi-Narrowband Illuminators","authors":"Chenggeng Zhao, Chuanbin Zang, Lianpeng Li, Heyue Huang, Xingpeng Mao","doi":"10.1049/rsn2.70139","DOIUrl":"https://doi.org/10.1049/rsn2.70139","url":null,"abstract":"Passive localisation with narrowband external illuminators is attractive for low-cost and flexible deployment, yet accurate positioning becomes difficult when time-delay (range) information is unreliable or unavailable. To address this limitation and leverage signals from multi-illuminator, this paper investigates range-free 3-D localisation for a monostatic passive radar. Doppler and DOA measurements (and, when available, Doppler-rate) are incorporated into a maximum-likelihood framework for joint position–velocity estimation. To mitigate the resulting high-dimensional optimisation, we propose an iteration based velocity estimation that expresses the velocity estimate as a function of a candidate position, reducing the original 6-D problem to a 3-D position search. A Gauss–Newton (GN) guided dimension-reduced Particle Swarm Optimisation (PSO) is then employed to accelerate convergence by steering elite particles along local GN directions while preserving global exploration. Simulation and measured-data results demonstrate that the proposed method achieves close to the Cramér-Rao Lower Bound (CRLB) accuracy with significantly improved efficiency, enabling stable localisation with fewer observations in both single-illuminator and multi-illuminator configurations.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708139","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

Multiple-Model Trajectory PMBM Filter for Tracking Manoeuvring Extended Targets 机动扩展目标跟踪的多模型弹道PMBM滤波器

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-04-06 DOI: 10.1049/rsn2.70135

Ibrahim Salim, Nermeen Okasha, Wagdy Anis

{"title":"Multiple-Model Trajectory PMBM Filter for Tracking Manoeuvring Extended Targets","authors":"Ibrahim Salim, Nermeen Okasha, Wagdy Anis","doi":"10.1049/rsn2.70135","DOIUrl":"https://doi.org/10.1049/rsn2.70135","url":null,"abstract":"This paper addresses the challenging problem of tracking multiple manoeuvring extended targets in cluttered environments by introducing the Multiple Model Extended Target Trajectory Poisson Multi-Bernoulli Mixture (MM-ET-TPMBM) filter. The proposed framework integrates the Jump Markov System (JMS) for motion mode switching with the trajectory random finite set (RFS) formalism, enabling simultaneous estimation of target trajectories, kinematic states, spatial extents and dynamic models within a unified Bayesian recursion. We derive closed-form prediction and update equations and present a computationally efficient implementation using gamma Gaussian inverse Wishart (GGIW) distributions for extended target representation. Comprehensive Monte Carlo simulations demonstrate that the MM-ET-TPMBM filter significantly outperforms existing methods, reducing the generalised optimal sub-pattern assignment (GOSPA) error by up to 53% and cardinality error by up to 71% while maintaining robust trajectory continuity and accurate model identification. The filter's principled approach and computational tractability make it suitable for demanding applications in autonomous navigation, surveillance and defence systems.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708041","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

Range Ambiguous Clutter Rank Estimation in Airborne Element-Pulse Coding-Multiple-Input Multiple-Output Radar 机载元件-脉冲编码-多输入多输出雷达距离模糊杂波秩估计

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-04-04 DOI: 10.1049/rsn2.70138

Di Song, Chunyu Song, Yue Zhao, Zhiyong Cheng, Shengyao Chen, Feng Xi

{"title":"Range Ambiguous Clutter Rank Estimation in Airborne Element-Pulse Coding-Multiple-Input Multiple-Output Radar","authors":"Di Song, Chunyu Song, Yue Zhao, Zhiyong Cheng, Shengyao Chen, Feng Xi","doi":"10.1049/rsn2.70138","DOIUrl":"https://doi.org/10.1049/rsn2.70138","url":null,"abstract":"Space-time adaptive processing (STAP) with element-pulse coding (EPC)-multiple-input multiple-output (MIMO) radars is capable of suppressing range ambiguous clutter in airborne radar fields. However, clutter rank has not been investigated in EPC-MIMO radars, which is of great significance for the design of STAP algorithms, such as reduced-rank and reduced dimension, etc. This paper studies the clutter rank estimation thoroughly. Firstly, a subspace transformation matrix including the entire clutter subspace is constructed, which is related to the spacing ratio of transmit and receive elements, the number of Doppler ambiguity and the number of range ambiguity. Secondly, the clutter rank is accurately estimated according to the subspace transformation matrix for any element spacing ratio, number of Doppler ambiguity, and number of range ambiguity. Moreover, based on the estimated clutter rank, the maximum resolvable number of range ambiguity is derived. Ultimately, the impact of channel weighting on clutter rank is analysed. Numerical results verify the accuracy of the estimated clutter rank and maximum resolvable number of range ambiguity.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70138","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147707999","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

MIMO Radar Detection Algorithms With Location Capabilities for Heterogeneous Environments 具有异构环境定位能力的MIMO雷达检测算法

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-04-01 DOI: 10.1049/rsn2.70127

Tianqi Wang, Chaoran Yin, Da Xu, Chengpeng Hao, Danilo Orlando, Giuseppe Ricci

{"title":"MIMO Radar Detection Algorithms With Location Capabilities for Heterogeneous Environments","authors":"Tianqi Wang, Chaoran Yin, Da Xu, Chengpeng Hao, Danilo Orlando, Giuseppe Ricci","doi":"10.1049/rsn2.70127","DOIUrl":"https://doi.org/10.1049/rsn2.70127","url":null,"abstract":"In this paper, we address the problem of joint adaptive detection and range-Doppler estimation for MIMO radar systems in heterogeneous environments. Specifically, we extend the Two-Step Generalised Likelihood Ratio Test Detector designed for homogeneous Gaussian background to work in compound-Gaussian scenarios. To this end, we replace the conventional sample covariance matrix of the secondary data with the estimators of clutter statistics based on the normalised sample covariance matrix and the recursive estimate. In this context, three detectors are proposed that can inherently provide estimates of target position in the delay-Doppler domain. Numerical examples demonstrate that the proposed detectors significantly outperform the homogeneous counterparts in heterogeneous environments whilst maintaining satisfying performance in homogeneous environments. Remarkably, the analysis of the false alarm rate reveals that the proposed detectors are insensitive to changes in the scaling factor and/or covariance structure of the background.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147707820","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

Multispectrally Constrained Waveform Design for MIMO Radar Direction Finding MIMO雷达测向的多频谱约束波形设计

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-03-31 DOI: 10.1049/rsn2.70104

Da Li, Xingyu Wang, Xiangyu Wu, Cheng Zhou, Yuxue Sun

{"title":"Multispectrally Constrained Waveform Design for MIMO Radar Direction Finding","authors":"Da Li, Xingyu Wang, Xiangyu Wu, Cheng Zhou, Yuxue Sun","doi":"10.1049/rsn2.70104","DOIUrl":"https://doi.org/10.1049/rsn2.70104","url":null,"abstract":"This paper focuses on the design of transmit waveforms for multiple-input-multiple-output (MIMO) radar systems in a spectrally crowded environment. The purpose is to improve the angle estimation performance of MIMO radar by minimising the asymptotic estimation bound of multiple signal classification (MUSIC) algorithm. To enhance the spectrum coexistence capabilities of radar system, a multispectral constraint is imposed on the sought waveforms. Moreover, a peak-to-average-power ratio (PAPR) constraint is enforced on the radar waveform to improve the practicability. To tackle the encountered nonconvex optimisation problem, a cyclic algorithm based on optimal covariance matrix matching (OCMM) and alternating direction method of multipliers (ADMM) are developed. The proposed algorithm first transforms the established optimisation problem into an OCMM optimisation problem with a more tractable structure, followed by the adoption of the ADMM method to obtain high-quality solution. Numerical examples are provided to show that the proposed algorithm can efficiently design the transmit waveforms, further improving the target localisation accuracy and enhancing the spectrum compatibility of the radar system.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708418","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

Radar Jamming Recognition Method Based on Cross-Modal Multilevel Feature Fusion 基于跨模态多水平特征融合的雷达干扰识别方法

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-03-29 DOI: 10.1049/rsn2.70111

Mingyu Wu, Mingjun Huang, Hao Wu, Kai Xie

{"title":"Radar Jamming Recognition Method Based on Cross-Modal Multilevel Feature Fusion","authors":"Mingyu Wu, Mingjun Huang, Hao Wu, Kai Xie","doi":"10.1049/rsn2.70111","DOIUrl":"https://doi.org/10.1049/rsn2.70111","url":null,"abstract":"Effective radar jamming recognition is a critical precondition for enhancing radar antijamming capabilities. Although deep neural networks have been widely adopted for this task, existing methods mainly rely on time-frequency (TF) maps, overlooking inherent signal features such as amplitude and phase. This incomplete representation leads to a significant decline in recognition accuracy under low jamming-to-noise ratio (JNR) and complex interference conditions. To address these challenges, we propose a cross-modal multilevel feature fusion network (CM-FF), which innovatively integrates one-dimensional signal tensors, spectrum and two-dimensional TF images to compensate for information loss in single-modal approaches, significantly enhancing feature separability and identification accuracy. A multilevel feature extraction module is proposed to extract multiscale features from both one-dimensional (1D) tensors and two-dimensional (2D) images. Besides, a multimodal feature fusion module is proposed to assign weights to different features adaptively. Experimental results show that our proposed method achieves a recognition accuracy of 98.4%, representing a maximum improvement of 14.6% over existing methods. Even under extremely low JNR conditions of −10 dB, our network maintains an accuracy rate of 80.75%. Furthermore, the network has fewer than 1 million parameters, demonstrating its lightweight design and low resource requirements.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147666327","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

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