Iet Radar Sonar and Navigation最新文献_第10页

Using lightweight denoising network to suppress multiple barrage jamming in range-Doppler domain 采用轻量降噪网络抑制距离-多普勒域多弹幕干扰

IF 1.4 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2024-10-17 DOI: 10.1049/rsn2.12645

Minghua Wu, Yupei Lin, Dongyang Cheng, Dan Song, Bin Rao, Wei Wang

{"title":"Using lightweight denoising network to suppress multiple barrage jamming in range-Doppler domain","authors":"Minghua Wu, Yupei Lin, Dongyang Cheng, Dan Song, Bin Rao, Wei Wang","doi":"10.1049/rsn2.12645","DOIUrl":"https://doi.org/10.1049/rsn2.12645","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Barrage jamming technology poses a significant threat to radar detection functions, yet there is limited research on anti-multiple barrage jamming using single-channel radar systems. Addressing this gap, this paper proposes a framework for anti-multiple barrage jamming based on range-Doppler domain denoising. This framework first performs pulse compression and Doppler processing on the echo signal to enhance the signal-to-jamming ratio. The range-Doppler spectrum data is then input into a deep denoising network to suppress multiple types of jamming. Additionally, the paper proposes a lightweight deep denoising network comprising a feature extraction module, a jamming suppression module, and a feature fusion module. The feature extraction module preliminarily extracts jamming features using multiple layers of depth-wise and point-wise convolution. The jamming suppression module removes noise at various resolutions through a lightweight encoding and decoding structure, effectively suppressing the barrage jamming signal. The feature fusion module uses convolution kernels with different sizes to merge the multiple features output by the jamming suppression module. Simulation results demonstrate that the proposed method effectively suppresses ten types of barrage jamming. Furthermore, a measured dataset is constructed to verify the method’s effectiveness.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 11","pages":"2308-2324"},"PeriodicalIF":1.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12645","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762675","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

An adaptive synthetic method for long sequence radar mode recognition 一种长序列雷达模式识别的自适应综合方法

IF 1.4 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2024-10-16 DOI: 10.1049/rsn2.12643

Xiaozhou Chen, Mengzhong Hu, Xiaobo Wang, Xuanze Liu, Xiangyang Lu

{"title":"An adaptive synthetic method for long sequence radar mode recognition","authors":"Xiaozhou Chen, Mengzhong Hu, Xiaobo Wang, Xuanze Liu, Xiangyang Lu","doi":"10.1049/rsn2.12643","DOIUrl":"https://doi.org/10.1049/rsn2.12643","url":null,"abstract":"<p>Radar work mode recognition is crucial to analyse radar behaviour and intention. There are some challenges limiting the recognition of long sequences with multiple mode classes. First, the performance of recognition method relies on precise segregation of intercepted sequence, which is often unfeasible in reality. Second, the states at the boundaries of adjacent modes may create extraneous mode samples that intervenes the recognition. Third, current methods fail to deal with the scenarios where multiple modes share the same state sequence. To address these problems, a novel forward matching method (FMM) is proposed, comprising a shortest path method (SPM) for intra-mode recognition, a matching strategy, and an adjustment mechanism. SPM is to provide potential recognition for short fragments of the given long sequence. The matching strategy is to assess the availability of current recognition. The adjustment mechanism tunes the segregation and improves the subsequent recognition. FMM offers several distinct advantages. First, the model can explicitly characterise the mode transition probability and is totally interpretable. Second, FMM can distinguish intentional ambiguities, alleviate mosaic ambiguity and probability deviation associated with inter-mode recognition. Third, FMM is extendable to integrate with other intro-mode recognition methods to cater to various scenarios.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 11","pages":"2155-2169"},"PeriodicalIF":1.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12643","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762782","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

Main lobe deceptive jamming suppression based on blind source separation and energy detection for monopulse radar 基于盲源分离和能量检测的单脉冲雷达主瓣欺骗性干扰抑制

IF 1.4 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2024-10-14 DOI: 10.1049/rsn2.12644

Zhenhua Liu, Wei Liang, Ning Fu, Liyan Qiao, Jun Zhang

{"title":"Main lobe deceptive jamming suppression based on blind source separation and energy detection for monopulse radar","authors":"Zhenhua Liu, Wei Liang, Ning Fu, Liyan Qiao, Jun Zhang","doi":"10.1049/rsn2.12644","DOIUrl":"https://doi.org/10.1049/rsn2.12644","url":null,"abstract":"<p>Main lobe deceptive jamming always causes the serious degradation of signal detection ability and angle measurement precision of monopulse radar. In recent years, the Blind Source Separation (BSS) method has been adopted to suppress the main lobe jamming. However, the separation results of BSS have the problem of amplitude ambiguity, which will cause the radar using monopulse angle measurement fail to measure the angle parameters after jamming suppression. To tackle this problem, a novel main lobe jamming suppression method is proposed based on BSS and energy detection. Firstly, the models of target echoes and jamming in the sum and difference beam receiving channels are derived. Secondly, the target echoes and interferences are separated by the Joint Approximate Diagonalisation of Eigenmatrices (JADE) algorithm, and then the unperturbed signal segments in the mixed signal are extracted by energy detection, thereby obtaining the precise ratio of the sum and difference channels to complete the angle measurement. Performance of the method was verified by numerical simulation. The results show that the proposed method can achieve interference suppression while accurately estimating the angle parameter of the target.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 11","pages":"2170-2181"},"PeriodicalIF":1.4,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12644","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762770","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

Multi-function radar work mode recognition based on residual shrinkage reconstruction recurrent neural network 基于残差收缩重构递归神经网络的多功能雷达工作模式识别

IF 1.4 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2024-10-11 DOI: 10.1049/rsn2.12650

Lihong Wang, Kai Xie

{"title":"Multi-function radar work mode recognition based on residual shrinkage reconstruction recurrent neural network","authors":"Lihong Wang, Kai Xie","doi":"10.1049/rsn2.12650","DOIUrl":"https://doi.org/10.1049/rsn2.12650","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>In modern electronic warfare, multi-function radar work mode recognition is increasingly crucial. However, the challenges posed by complex electromagnetic environments, such as lost pulses, spurious pulses, and measurement errors, along with the reliance of traditional multi-task learning strategies on clean samples, make it difficult for existing algorithms to achieve satisfactory recognition performance in real-world scenarios. To address these issues, this paper introduces a novel residual shrinkage reconstruction recurrent neural network (RS-RRNN). The network uses a Gated Recurrent Unit as its backbone to extract temporal features and enhances feature extraction by reconstructing the GRU's input, while also reducing dependence on clean samples. These features are then processed through a residual shrinkage structure to reduce noise, which significantly improves the model's robustness in non-ideal scenarios. Simulations demonstrate that RS-RNN has better performances in accuracy and robustness than existing networks.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 11","pages":"2362-2376"},"PeriodicalIF":1.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12650","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762227","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

Efficient multi-perspective jamming feature perception network for suppressive jamming recognition with limited training samples 基于多视角干扰特征感知网络的有限训练样本抑制干扰识别

IF 1.4 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2024-10-10 DOI: 10.1049/rsn2.12647

Minghua Wu, Yupei Lin, Dongyang Cheng, Xiaohai Zou, Bin Rao, Wei Wang

{"title":"Efficient multi-perspective jamming feature perception network for suppressive jamming recognition with limited training samples","authors":"Minghua Wu, Yupei Lin, Dongyang Cheng, Xiaohai Zou, Bin Rao, Wei Wang","doi":"10.1049/rsn2.12647","DOIUrl":"https://doi.org/10.1049/rsn2.12647","url":null,"abstract":"<p>Recognising suppressive jamming signals is crucial for radar systems to counteract this type of jamming, highlighting the importance of research in this area. Current deep learning-based methods for identifying suppressive jamming signals suffer from reduced effectiveness with limited training samples and issues related to high parameter counts and computational complexity. To address these challenges, the authors propose a jamming recognition method based on an efficient multi-perspective jamming feature perception network. This method extracts features from the time-frequency spectrum of jamming signals from multiple perspectives, including local, multi-scale, cross-space, and global, to obtain more robust and discriminative jamming features and improve recognition under limited training sample conditions. Additionally, the authors design efficient modules for local jamming feature extraction, multi-scale jamming feature down-sampling, and global jamming feature representation. The lightweight design of these modules enables the proposed method to maintain excellent jamming recognition performance while reducing parameters and computational complexity. Simulation experiment outcomes highlight the exceptional effectiveness of the proposed technique across multiple metrics compared to eight other approaches. Furthermore, the proposed method exhibits significantly fewer parameters and lower computational complexity than its deep learning-based counterparts.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 11","pages":"2333-2348"},"PeriodicalIF":1.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12647","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762122","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

Wideband beam domain sparse Bayesian learning passive focusing localisation algorithm 宽带波束域稀疏贝叶斯学习被动聚焦定位算法

IF 1.4 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2024-10-06 DOI: 10.1049/rsn2.12642

Hao Wang, Hong Zhang, Qiming Ma, Shuanping Du

引用次数: 0

Random matrix extended target tracking for trajectory-aligned and drifting targets 轨迹对准和漂移目标的随机矩阵扩展目标跟踪

IF 1.4 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2024-10-04 DOI: 10.1049/rsn2.12628

Kurtuluş Kerem Şahin, Ali Emre Balcı, Emre Özkan

引用次数: 0

Broadband multi-carrier linear frequency modulation signal reception with subcarrier frequency offset deramp processing 宽带多载波线性调频信号接收与副载波频偏脱模处理

IF 1.4 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2024-10-02 DOI: 10.1049/rsn2.12646

Jinhu Li, Fangzheng Zhang, Jiayuan Kong, Shilong Pan, Yuhui He

{"title":"Broadband multi-carrier linear frequency modulation signal reception with subcarrier frequency offset deramp processing","authors":"Jinhu Li, Fangzheng Zhang, Jiayuan Kong, Shilong Pan, Yuhui He","doi":"10.1049/rsn2.12646","DOIUrl":"https://doi.org/10.1049/rsn2.12646","url":null,"abstract":"<p>In this paper, a broadband multi-carrier linear frequency modulation (LFM) signal reception method with subcarrier frequency offset deramp processing is proposed and investigated. The proposed frequency offset deramp processing is implemented by mixing the multi-carrier LFM radar echo with a multi-carrier LFM reference that has a different subcarrier frequency interval. With this design, the sampling rate of the radar receiver is remarkably reduced and crosstalk-free separation of different subcarrier signals is easily conducted in the frequency domain. To fuse the multiple subcarriers and fill the frequency gaps, a sparse reconstruction method is employed to obtain the broadband response, which is essential for achieving high range resolution detection. The effectiveness of the proposed method is validated through an experiment in which the reception of an 8-carrier LFM signal is conducted and a total bandwidth of 6 GHz after multi-carrier fusion is demonstrated. An inverse synthetic aperture radar imaging experiment is also conducted with the results verifying the good potential of the proposed method in practical applications.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 11","pages":"2325-2332"},"PeriodicalIF":1.4,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759859","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

An asymptotically unbiased 3D source localisation method based on frequencies and angles measurements 基于频率和角度测量的渐近无偏三维光源定位方法

IF 1.4 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2024-09-27 DOI: 10.1049/rsn2.12637

Chenggeng Zhao, Heyue Huang, Xingpeng Mao, Junjie Lang, Xiuquan Dou

{"title":"An asymptotically unbiased 3D source localisation method based on frequencies and angles measurements","authors":"Chenggeng Zhao, Heyue Huang, Xingpeng Mao, Junjie Lang, Xiuquan Dou","doi":"10.1049/rsn2.12637","DOIUrl":"https://doi.org/10.1049/rsn2.12637","url":null,"abstract":"<p>Based on frequency of arrival (FOA) and angle of arrival (AOA) measurements, the localisation of a moving source using a number of stationary stations is discussed. A closed-form solution and bias reduction method using FOA and AOA measurements will be presented to quickly and accurately estimate target parameters, including location and velocity. The closed-form solution is implemented using two-stage weighted least squares, which constructs a pseudolinear equation by introducing auxiliary variables to perform linear estimation. In the process of linearisation, the authors utilise AOA measurements to simplify the FOA pseudolinear equation and reduce the number of auxiliary parameters. This means that fewer stations are needed to estimate the target parameters. However, the use of the computationally attractive pseudolinear formulation will introduce a non-ignorable localisation bias if the measurements are not sufficiently accurate. To solve the above problem, a quadratic constraint on least squares minimisation is considered in the bias reduction method. Under moderate Gaussian noise, theoretical analysis and simulation results show that the root mean square error of proposed method can significantly reduce positioning deviation and asymptotically approach the Cramer–Rao Lower Bound. <span>Keywords</span> Radar, Radar detection, Doppler shift, Parameter estimation.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 11","pages":"2281-2294"},"PeriodicalIF":1.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759882","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

Deception mechanisms of FDA‒AWACS against passive monopulse angle measurements FDA-AWACS对被动单脉冲角测量的欺骗机制

IF 1.4 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2024-09-25 DOI: 10.1049/rsn2.12638

Bo Wang, Gang Wang, Yonglin Li, Rennong Yang, Yu Zhao

{"title":"Deception mechanisms of FDA‒AWACS against passive monopulse angle measurements","authors":"Bo Wang, Gang Wang, Yonglin Li, Rennong Yang, Yu Zhao","doi":"10.1049/rsn2.12638","DOIUrl":"https://doi.org/10.1049/rsn2.12638","url":null,"abstract":"<p>Airborne warning and control systems (AWACS) serve as critical command and control centres in air combat operations, making them prime targets for strategic attacks. These enemy attacks typically rely on the accurate determination of AWACS combat positions using different direction-finding devices. In particular, passive monopulse angle measurement systems locate AWACS by measuring the angles of signals emitted by AWACS radiation sources, thus rendering them vulnerable to attacks. Given the criticality of the airborne radars of AWACS in battle command and control operations, they must function continuously to monitor air and sea targets. Hence, AWACS cannot effectively evade electronic reconnaissance systems through tactics such as radar shutdown. To explore alternative measures, the authors investigate the deception mechanisms of an integrated frequency diverse array AWACS (FDA‒AWACS) against passive monopulse angle measurements. Using an established FDA signal model, the principles underlying two common monopulse angle measurement methods are first outlined. Subsequently, the angle estimation formulae typically used by these monopulse angle measurement systems to interpret received FDA radiation signals are derived. Additionally, the transmit beampatterns, amplitude patterns, and angle measurement deception capabilities of several typical FDAs are examined. Simulation results indicate that the FDA‒AWACS can theoretically deceive passive monopulse angle measurement systems to a certain extent. However, one-dimensional uniform linear FDAs and other arrays using sinusoidal frequency offsets exhibit limited deception abilities. In contrast, arrays utilising cubic and quartic frequency offset achieve angle measurement errors exceeding 2° in far-field scenarios.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 11","pages":"2264-2280"},"PeriodicalIF":1.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12638","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762586","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