Iet Radar Sonar and Navigation最新文献

{"title":"Centralised Fusion of Cooperative Sensors With Limited Field of View for Multiple Resolvable Group Targets Tracking","authors":"Xirui Xue,&nbsp;Jikun Ye,&nbsp;Daozhi Wei,&nbsp;Shucai Huang,&nbsp;Changxin Luo,&nbsp;Ning Li,&nbsp;Ruining Luo","doi":"10.1049/rsn2.70032","DOIUrl":"10.1049/rsn2.70032","url":null,"abstract":"<p>The coordinated deployment of multi-sensor systems significantly enhances group target detection capabilities, yet persistent tracking remains challenging due to inherent limitations in single-sensor field of view (FoV) coverage. This paper proposes a novel labelled multi-Bernoulli (LMB) filter for resolvable group target (RGT) tracking under the centralised fusion (CF) framework, abbreviated as the CF-LMB-RGT filter. The proposed method introduces the virtual leader kinematic model to capture intra-group motion constraints and incorporates group structure undirected graph into the LMB recursion for interaction prediction. A key innovation lies in the Kullback–Leibler divergence minimised fusion rule that optimally integrates local posteriors within joint FoV regions while explicitly modelling common FoV overlaps, enabling complementary information fusion across nonoverlapping sensor FoVs. Simulation results demonstrate that our method achieves impressive tracking accuracy for RGTs by integrating information from all sensors.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074264","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}

{"title":"Fault Modes and Methods to Evaluate Integrity Risk for FastSLAM-Based Navigation","authors":"Pil Hun Choi,&nbsp;Gihun Nam,&nbsp;Dongchan Min,&nbsp;Noah Minchan Kim,&nbsp;Jiyun Lee","doi":"10.1049/rsn2.70029","DOIUrl":"10.1049/rsn2.70029","url":null,"abstract":"<p>The fast simultaneous localisation and mapping (FastSLAM), utilising the Rao-Blackwellised particle filter, provides a robust navigation solution in urban environments. Ensuring the integrity of FastSLAM is critical for the safety of autonomous driving applications. Our previous work proposed an empirical integrity risk evaluation method for nominal conditions and a probabilistic bound using PAC (probably approximately correct)–Bayesian theory. However, it was limited by overly conservative risk estimates and a lack of consideration for fault conditions. This study introduces a refined integrity evaluation framework with three main contributions. First, a modified weighting and resampling technique is proposed to reduce conservatism in empirical risk without compromising estimation accuracy. Second, a fault monitoring method is introduced to detect and isolate control input faults during the dynamic update step. Third, a conservative integrity risk evaluation approach is developed for FastSLAM to account for data association faults using probabilistic modelling. Simulation results show that the proposed methods significantly improve integrity performance under both nominal and faulted scenarios.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074263","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}

{"title":"Statistical Analysis of Performance of Optimisation-Based SAR Autofocus","authors":"Patrick Haughey,&nbsp;Mikhail Gilman,&nbsp;Semyon Tsynkov","doi":"10.1049/rsn2.70030","DOIUrl":"10.1049/rsn2.70030","url":null,"abstract":"<p>Transionospheric SAR autofocus is a variational algorithm designed to circumvent the deficiencies of conventional autofocus techniques in correcting the distortions of spaceborne SAR images due to ionospheric turbulence. It has demonstrated superior performance in a variety of computer-simulated imaging scenarios. In the current work, we conduct a systematic statistical analysis of transionospheric SAR autofocus aimed at corroborating its robustness and identifying limitations and sensitivities across a broad range of factors that affect the autofocus performance. We employ the range-compressed domain representation where the target reflectivity, antenna signal, and the phase screen depend only on the azimuthal coordinate. The three main factors included in the study are the levels of turbulent perturbations, clutter, and noise. We use the normalised cross correlation (NCC), integrated sidelobe ratio (ISLR), and peak desynchronisation (PD) as a-posteriori performance metrics. A key objective of the current analysis, beyond assessing the autofocus performance, is to identify the directions of how to further improve the algorithm, in terms of both the quality of focusing and associated computational cost.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932352","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}

{"title":"Joint Optimal Allocation of Resources for Multiple Jammer Based on Multi-Agent Deep Reinforcement Learning","authors":"Jieling Wang,&nbsp;Yanfei Liu,&nbsp;Chao Li,&nbsp;Zhong Wang,&nbsp;Yali Li","doi":"10.1049/rsn2.70031","DOIUrl":"10.1049/rsn2.70031","url":null,"abstract":"<p>In response to the complex scenario where multiple jammers navigate through a netted radar system (NRS), this study presents an optimised allocation algorithm for cooperative jamming resources, namely the Multi-Agent Jamming Resource Allocation (MJCJRA) algorithm, which is based on multi-agent deep reinforcement learning. Initially, the research develops a target fusion detection probability function and a global performance index optimisation function, which are tailored to the specific jamming and radar detection models of the scenario. Subsequently, the multiple jammers are mapped into a multi-agent system with a greedy strategy employed to generate targeted rewards for the jamming agents, enhancing their learning efficiency and performance. The study culminates in the design of evaluation and mixed-strategy networks for the jamming agents. It utilises an exponential mean shift method for soft updates of the target network, adopts priority experience replay and importance sampling methods, and incorporates reward centring into the loss function for network updates. Experimental findings demonstrate that MJCJRA algorithm significantly surpasses the baseline method, the particle swarm optimisation (PSO), the snow ablation optimiser (SAO), the multi-agent deep deterministic policy gradient (MADDPG) and multi-agent proximal policy optimisation (MAPPO), effectively diminishing the detection capability of NRS.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143905140","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}

{"title":"Dynamic RCS Simulation Using Active Frequency Selective Surface","authors":"Dejun Feng,&nbsp;Yumeng Fang,&nbsp;Yameng Kong,&nbsp;Junjie Wang,&nbsp;Liwei Chen","doi":"10.1049/rsn2.70027","DOIUrl":"10.1049/rsn2.70027","url":null,"abstract":"<p>In the experimental test of the radar system, it is extremely important to build a realistic experimental environment for electromagnetic target testing, which is often realised by the radar target simulation technology. Corner reflectors often simulate radar RCS features by the spatial arrangement; however, their electromagnetic characteristics are solidified and the RCS features differ from those of real targets. This paper proposes a target RCS simulation method based on AFSS echo power modulation. The core idea is to use AFSS reflection modulation to dynamically regulate the target power information to achieve flexible and fast control of the target radar RCS characteristics. Based on the AFSS echo power modulation model, the theoretical relationship between the modulation parameters and the RCS value is deduced, and the duty cycle of the scattering state control signal is used as an adjustable variable to realise the simulation of the dynamic RCS sequence of the mid-range target. The RCS simulation experiment is carried out based on the target measured data, and the simulation effect is analysed in terms of statistical characteristics and similarity coefficients. The simulation results show that the statistical characteristics of the simulated RCS sequence and the target RCS sequence are very close to each other with the mean value and standard deviation within 1 dBsm and the extreme value and extreme deviation within 3 dBsm. The method is of great significance in the field of radar system tests and electronic protection.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143905138","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}

{"title":"Design of Spectrally Compatible Waveforms With Low Auto- and Cross-Correlation-Weighted Integrated Sidelobe Levels","authors":"Zhaobo Jia,&nbsp;Lei Yu,&nbsp;Yinsheng Wei","doi":"10.1049/rsn2.70024","DOIUrl":"10.1049/rsn2.70024","url":null,"abstract":"<p>Low-correlation sidelobes are critical for spectrally compatible waveforms in multiple-input multiple-output (MIMO) radar systems. This study presents a novel algorithm for designing spectrally compatible waveforms for MIMO radar with low auto- and cross-correlation sidelobes to enhance weak target detection capability. We adopt the minimum auto- and cross-correlation-weighted integrated sidelobe level (ACWISL) as the objective function. Under spectral and constant modulus constraints, we formulate a nondeterministic polynomial time (NP)-hard problem. To solve this problem, we combine the block successive upper-bound minimisation (BSUM) and majorisation-minimisation (MM) algorithms to develop the BSUM-MM algorithm. The original problem is decomposed into several independent subproblems, which are iteratively solved using the MM algorithm. We also employ the fast Fourier transform (FFT) to significantly accelerate the calculation. Simulation results demonstrate that the proposed algorithm is superior in terms of computational efficiency and sidelobe performance.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143905139","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}

{"title":"Long-Tailed Distributed Radar Emitter Signal Automatic Modulation Recognition Based on Decoupled Training","authors":"Gangyin Sun,&nbsp;Shiwen Chen,&nbsp;Li Zhang,&nbsp;Chaopeng Wu,&nbsp;Haikun Fang","doi":"10.1049/rsn2.70026","DOIUrl":"10.1049/rsn2.70026","url":null,"abstract":"<p>The existing radar emitter modulation recognition methods typically assume that the data distribution across different types is balanced. But in reality, the number of signals of various kinds often follows a long-tail distribution, leading to model overfitting for the head classes and underfitting for the tail classes. As a result, the overall recognition performance of models under such data imbalances is suboptimal. A long-tail distribution automatic modulation recognition method based on decoupled training is proposed to address this issue. Based on the ResNeXt network, the proposed method decouples the model training process into two stages: a feature extraction phase under the imbalanced dataset and the classifier learning stage under a balanced dataset. The classifier boundary is fine-tuned by <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>τ</mi>\u0000 </mrow>\u0000 <annotation> $tau $</annotation>\u0000 </semantics></math>-normalization method. Compared to existing radar emitter modulation recognition frameworks, the proposed method achieves an overall recognition accuracy of 86.8% when the data imbalance factor is 0.01, surpassing the baseline model by 5%, and improves the performance of radar emitters modulation recognition in the real environment.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892921","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}

{"title":"Guest Editorial: Selected Papers From Radar 2023—Dreaming the Radar Future","authors":"Brian W.-H. Ng,&nbsp;Elias Aboutanios,&nbsp;Luke Rosenberg,&nbsp;Marco Martorella","doi":"10.1049/rsn2.70023","DOIUrl":"10.1049/rsn2.70023","url":null,"abstract":"&lt;p&gt;It is our great pleasure to introduce a series of extended papers from the 2023 IEEE International Radar Conference (RADAR 2023), 6–10 November 2023, held in Sydney, Australia. The conference theme was Dreaming the Radar Future. Befitting this theme, the conference received a diverse range of contributions from leading international researchers. A total of 200 full papers were published in the proceedings of RADAR 2023. A selection of authors from the best papers, including the winners and finalists of the best paper and best student paper competitions, were invited to submit extended journal papers for this special issue. The extended papers were required to contain at least 40% new material compared with the conference submissions and were subjected to a separate peer-review process, conducted with the same rigour as regular issues of &lt;i&gt;IET Radar&lt;/i&gt;, &lt;i&gt;Sonar &amp; Navigation&lt;/i&gt;.&lt;/p&gt;&lt;p&gt;This special issue contains 8 papers, across a wide range of application areas. These include space domain awareness, distributed radar sensor networks, flying target detection from SAR images, clutter processing, drone characterisation, track confirmation, polarimetry for target imaging and over the horizon radar.&lt;/p&gt;&lt;p&gt;Achieving synchronisation presents a major challenge for the deployment of distributed network of radars. Addressing this problem can unleash the vast potential of the sensor network. Kenney et al. [&lt;span&gt;1&lt;/span&gt;] present a decentralised technique for attaining frequency, time and phase synchronisation across a distributed network. The presented approach builds on a previously published method for correcting phase and clock bias, and has the advantage of not requiring RF hardware upgrades. A comprehensive theoretical analysis is presented in this paper, along with simulations, that show the proposed technique approaches the theoretical performance limit. A beamforming scenario is used to illustrate how the proposed technique can be implemented to solve a practical problem.&lt;/p&gt;&lt;p&gt;Tracking evasive targets present a great challenge to modern radar systems, particularly for radar resource management. This problem is highly complex, with multiple agents affecting many scenarios. Dolinger et al. [&lt;span&gt;2&lt;/span&gt;] present an approach to this problem with reinforcement learning set within a game theoretic context. Three game theory strategies are implemented and tested in a simulated radar environment, with the performance compared against heuristic methods. The results show that collaborative methods achieve greater performance, and that finer nuances in the performance that point towards future research directions.&lt;/p&gt;&lt;p&gt;Howard and Nguyen [&lt;span&gt;3&lt;/span&gt;] present a collection of techniques for manipulating the radar ambiguity function for over the horizon radar. They present a novel characterisation of the ambiguity function in terms of twisted convolutions and show how it can be transformed by an area preserving linear transformation of the dela","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880164","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}

{"title":"Fusion of HRRP Time-Frequency Analysis and Multi-Scale Features for Convolutional Neural Network-Based Target Recognition","authors":"Xiaohui Wei,&nbsp;Zhulin Zong","doi":"10.1049/rsn2.70019","DOIUrl":"10.1049/rsn2.70019","url":null,"abstract":"<p>For radar target recognition in high-resolution range profiles (HRRP) under low signal-to-noise ratio (SNR) conditions, traditional methods typically involve denoising followed by recognition. However, these methods struggle with complex noise. To enhance HRRP information extraction, this paper proposes an integrated approach combining noise reduction and recognition. First, the short-time Fourier transform (STFT) is improved with a complex Gaussian window to enhance time-frequency resolution. Then, multi-scale analysis is applied by introducing scale values to better capture detailed target features. Differential operations are used to highlight scattering points and edges, improving recognition accuracy. A convolutional neural network (CNN) is employed to extract multi-level features for target recognition. Experimental results on a simulated HRRP dataset from the U.S. Air Force Research Laboratory (AFRL) demonstrate the proposed method's superior performance. It outperforms traditional methods in both accuracy and robustness, offering stronger noise resistance and better utilisation of HRRP's rich features, providing an effective solution for radar target recognition tasks.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861544","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}

{"title":"Power and Waveform Resource Allocation Method of LPI Netted Radar for Target Search and Tracking","authors":"Longhao Xie,&nbsp;Wenxing Ren,&nbsp;Ziyang Cheng,&nbsp;Ming Li,&nbsp;Huiyong Li","doi":"10.1049/rsn2.70022","DOIUrl":"10.1049/rsn2.70022","url":null,"abstract":"<p>A joint power and waveform resource allocation algorithm is proposed for netted radar integrated search and tracking tasks with low probability of intercept. For the search and tracking performance, the detection probability and the posterior Cramér-Rao lower bound of the target are adopted separately. The optimization problem of joint resource allocation is solved by controlling the radar node selection, power allocation, waveform selection, and pulse duration, to minimise the total power of the netted radar while meeting the search and tracking performance for a given target. The intelligent optimization methods are used to solve the problem, and the effectiveness of the proposed method is verified by simulation.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850985","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}