Iet Radar Sonar and Navigation最新文献

{"title":"Guest Editorial: Multistatics and passive radar","authors":"Matthias Weiss, Diego Cristallini, Daniel O'Hagan","doi":"10.1049/rsn2.12683","DOIUrl":"https://doi.org/10.1049/rsn2.12683","url":null,"abstract":"<p>Welcome to the special issue of ‘<i>Multistatic and Passive Radar</i>’. The motivation for this special issue stems from the long running, biennial, Multistatics and Passive Radar Focus Days organised and hosted by Fraunhofer FHR in Wachtberg, Germany. This exciting collection brings together cutting-edge research in multistatic radar, passive radar, and related topics from the 2023 Focus Days. Multistatic radar systems employ multiple transmitters and/or receivers, which offer several advantages over traditional monostatic radars. These include wide area surveillance, improved accuracy, and enhanced resistance to jamming and spoofing attacks.</p><p>The need for advanced radar has increased in recent years due to the proliferation of modern threats, such as stealth aircraft, missiles and small drones. Passive radars provide an attractive complement to monostatic systems in that they utilise signals emitted by pre-existing, often communications, transmitter infrastructure. The spatial separation between transmitter and receiver/s offers a high degree of sensor-sanctuary for the passive radar receiver. In addition, passive radars do not increase EM spectrum congestion. They have gained considerable attention in recent years due to their potential for varied defence and security applications.</p><p>This special issue aims to highlight the latest advances, challenges, and opportunities in multistatic and passive radar systems. By bringing together leading experts from academia and industry, we hope to provide valuable insights into current research trends and future directions for these important fields of study. We hope that this special issue serves as a valuable resource for researchers, engineers, and practitioners working in the field of radar technology, inspiring new ideas and collaboration among them.</p><p>In this Special Issue you will find papers, all of which underwent peer review, that cover different topics linked to multistatic and passive radar.</p><p>The accepted papers may be clustered into four main categories, namely employing emerging communications systems for new multistatic setups, synchronisation between spatially distributed nodes, tracking, and answer the question of how best to present bistatic/multistatic results to the end-user. The papers which falls into the first category deals with new communications waveforms and show in theory and practical applications the achieved results. The papers that constitute the first category are from Guenin et al. and Maksymiuk et al. The second category of papers investigates the problem of exchanging time information among the netted nodes in a multistatic setup. These papers are from Valdes et al. and Busley et al. The third category of papers deals with the problem to correct labelling of targets and tracking them in a multistatic constellation. These papers are from Penggang et al., two contributions from Guan et al, and one from Tang et al. The fourth category proposes a new me","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2397-2399"},"PeriodicalIF":1.4,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12683","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253333","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":"Clutter rank estimation for the multistage Wiener filter","authors":"Rachel Gray,&nbsp;Elias Aboutanios,&nbsp;Luke Rosenberg,&nbsp;Josef Zuk","doi":"10.1049/rsn2.12689","DOIUrl":"https://doi.org/10.1049/rsn2.12689","url":null,"abstract":"<p>In airborne radar, reduced rank detection techniques are used when there are insufficient samples available for fully adaptive processing. This is the case in maritime radar, where the data can be both non-stationary and non-homogeneous. There are several approaches that have been proposed to address this problem. These include the single data set algorithms that eliminate the need for training data and reduced rank detectors such as principal components, cross-spectral metric and the multistage Wiener filter (MWF). This latter approach is superior to other rank reduction techniques in terms of computational efficiency and sample support requirements. In this paper, the authors propose an algorithm that determines the rank of the sea clutter and relates it to the number of stages in the MWF. The algorithm presented is formulated as a model order estimation problem that utilises the minimum description length (MDL). The authors present a computationally efficient implementation of the MDL and demonstrate its effectiveness using simulated data.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363008","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":"Editorial: RSN Editorial 2025","authors":"Hugh Griffiths,&nbsp;Alessio Balleri","doi":"10.1049/rsn2.12690","DOIUrl":"https://doi.org/10.1049/rsn2.12690","url":null,"abstract":"<p>The SI programme has resulted in coherent, high-quality collections of papers that are easy to locate.</p><p>We welcome proposals for Special Issues from potential Guest Editors, but emphasise that papers in Special Issues undergo the same degree of review and scrutiny as regular papers.</p><p>We have an exciting SI schedule for 2025, and we encourage our readers and authors to browse the journal website and submit their papers to these.</p><p>In 2025 the IET <i>RSN</i> journal is moving to a continuous publication model. This means that articles are published in one volume/one issue each year and so papers will not be grouped into separate multiple issues within a volume.</p><p>Articles will be published in either ascending or descending order of the date of publication. Articles assigned to a Special Issue will be published in the issue when ready and grouped together in a virtual collection.</p><p>The change to a continuous publication model complements our transition of the journal to Gold Open Access in 2020.</p><p>The editors of the IET <i>RSN</i> journal have always striven to maintain a reputation for rapid processing and publication. Statistics for 2024 (to the end of November) show that the median time from submission to first decision is 25 days, and from acceptance to e-first publication is 29 days. This achievement has depended on rapid responses by our reviewers, and we express our gratitude to the reviewers for their time, expertise and hard work in refereeing papers and maintaining the reputation of the journal, both for quality and for rapid publication. This is an essential part of the publication process, but necessarily the reviewers must remain anonymous. This editorial is one place where we can acknowledge their contribution and record our thanks to them.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12690","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118603","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":"High sensitivity multi-channel digital receiver for wideband very weak signal direction-finding classified by machine learning","authors":"Chen Wu,&nbsp;Michael Low","doi":"10.1049/rsn2.12685","DOIUrl":"https://doi.org/10.1049/rsn2.12685","url":null,"abstract":"<p>For weak signal detection with direction-finding (DF), this article presents a new receiver design approach that combines our accumulatively increasing receiver sensitivity (AIRS) signal detection algorithm with the compressive-sensing (CS)-based DF-array/algorithm. The former uses the concept of timeslot (TS)-based signal threshold detection, whereas the latter employs a frequency-independent array with randomly located elements, whose bandwidth (BW) largely determines the DF-array BW. To estimate the direction of a signal, the AIRS algorithm generates the array steering vectors in each TS when the amplitude of any frequency bins exceeds the predetermined threshold of the TS. The aim of this paper is to demonstrate the ability of the new receiver to detect low probability of intercept radar signals with high DF accuracy, fine frequency resolution, and good time-of-arrival measurement resolution. To discriminate accurate emitter directions from many false estimations created by the DF-array in very low signal-to-noise ratio environments, K-means clustering was also applied. In a scenario, the frequency modulated signals from several 165-mW X-band radars were in the field of view of a 6-element DF-array. Simulation results show that the receiver can accurately estimate all the emitters' directions with root mean squared error of less than 1°, when the separation between the DF-array and radars is about 100 km.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12685","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380940","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":"ELoran signal message recognition algorithm based on GTCN-transformer","authors":"Kai Zhang,&nbsp;Fan Yang,&nbsp;Weidong Wang,&nbsp;Bingqian Wang","doi":"10.1049/rsn2.12688","DOIUrl":"https://doi.org/10.1049/rsn2.12688","url":null,"abstract":"<p>The Enhanced Long Range Navigation (eLoran) system serves as a crucial backup to the Global Navigation Satellite System (GNSS), leveraging advantages, such as low signal frequency, high transmitter power, and stable propagation distance. However, the prevailing demodulation techniques employed by the eLoran system, which are largely based on conventional digital signal processing, are susceptible to substantial inaccuracies when confronted with intense interference and complex environmental conditions. This paper introduces a novel GTCN-Transformer network designed for the specific task of recognising message in eLoran pulse group signal. The network is constructed by enhancing the architecture of Temporal Convolutional Networks (TCN) and integrating the Transformer mechanism. In order to extract significant features from the pulse group signal, a sequence dataset was obtained by using cepstral analysis. Subsequently, the GTCN-Transformer network is deployed to recognise the message contained within the eLoran pulse group signal. The experimental results demonstrate that the GTCN-Transformer network achieves a recognition accuracy of over 95% for eLoran signal message information when the SNR exceeds 10 dB, even in the presence of sky-wave and cross-interference signals. Moreover, a comparative analysis with recurrent neural network (RNN) reveals that the GTCN-Transformer network outperforms these architectures in terms of recognition accuracy.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12688","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362387","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":"Concept and theoretical performance analysis for decentralised digital synchronisation in distributed radar sensor networks","authors":"Russell H. Kenney,&nbsp;Justin G. Metcalf,&nbsp;Jay W. McDaniel","doi":"10.1049/rsn2.12687","DOIUrl":"https://doi.org/10.1049/rsn2.12687","url":null,"abstract":"<p>This paper presents a decentralised technique for achieving frequency, time, and phase synchronisation of platforms cooperating in a distributed radar sensor network. The proposed method is advantageous for existing digital radar systems as it can be implemented entirely in the software without the use of additional RF hardware required by other techniques. The synchronisation signal model for signals transmitted and received in various clock domains is presented and an estimation model is subsequently derived for estimating and correcting the clock drifts. A modified version of a previously developed phase and clock bias correction procedure is outlined for correcting time and phase after frequency synchronisation. A comprehensive theoretical performance analysis of the technique is performed in which the expected maximum achievable performance is derived in terms of the Cramér–Rao lower bound for frequency, time, and phase measurements. Multiple Monte Carlo simulations show that the proposed technique approaches this performance limit. Finally, a simulated distributed transmit beamforming scenario is provided to show the application of the proposed technique in a practical system architecture. The results of this show that as the signal-to-noise ratio approaches moderate levels, the proposed synchronisation technique enables the beamforming network to achieve nearly optimal coherent energy gain at the beamforming destination.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363081","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":"Detecting flying objects in synthetic aperture radar images using Moving Target Indicator methods","authors":"Elliot J. Hansen,&nbsp;Brian W.-H. Ng,&nbsp;Mark Preiss","doi":"10.1049/rsn2.12676","DOIUrl":"https://doi.org/10.1049/rsn2.12676","url":null,"abstract":"<p>The growing proliferation of synthetic aperture radar (SAR) sensors brings the tantalising prospect of extending their utility into ‘novel’ applications. One potential extension is the detection of fast moving and accelerating flying objects in SAR imagery. However, since SAR image formation typically assumes the scene to be static over the coherent processing interval, moving objects give rise to blurred point spread functions, significant range migration and even potential aliasing of target signatures. The result is reduced target to clutter ratio (TCR) and poor detection performance. Successful detection of airborne targets thus requires compensation for potentially large target acceleration and velocity values observed over the comparatively long dwell times typical of practical SAR collection paradigms. This paper considers this problem and presents two main ideas to achieve this goal: a carefully constructed Moving Target Indicator (MTI) detection method implemented using real-world Ingara SAR data, and a theoretical ground clutter suppression method. The MTI detection method combines several well-known techniques for the flying target detection problem: interferometric processing, clutter suppression, and autofocus, and provides an extended acceleration phase compensation technique for highly accelerating targets such as planes. This proposed processing pipeline has been applied to experimental data of a plane during take off (a challenging Doppler unambiguous moving target), with the goal of continued detecting and tracking of this target. A generalised SAR signal model is presented that parameterises a flying moving target signature in terms of range and azimuthal target velocities and accelerations. Data driven approaches for estimating these motion parameters are examined and applied to experimental data acquired with the Ingara SAR sensor. The detection method was found to improve TCR by around 6 dB, along with superior detection and tracking performance. Following this, a theoretical study into suppressing ground clutter via multi-channel cross-track interferometry is investigated. Three separate ground clutter suppression methods, coherent subtraction, conventional beamforming, and minimum variance distortionless response (MVDR) beamformer, are presented then analysed using stochastic simulations. The MVDR adaptive beamformer method was found to provide the best performance for the scenario simulated.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12676","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363026","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":"A Gaussian Unscented Kalman Filter algorithm for indoor positioning system using Ultra Wide Band measurement","authors":"DaLong Sun,&nbsp;Mingsheng Wei,&nbsp;Yiyang Lyu,&nbsp;Di Wang,&nbsp;Shidang Li,&nbsp;Wenshuai Li,&nbsp;Lei He,&nbsp;Shihu Zhu","doi":"10.1049/rsn2.12682","DOIUrl":"https://doi.org/10.1049/rsn2.12682","url":null,"abstract":"<p>In order to further improve the accuracy of the non-linear positioning model in the research of ultra wide band (UWB) indoor positioning, a Gaussian unscented Kalman filter (GUKF) algorithm is proposed in this paper. This localisation algorithm first uses a Gaussian function to design a Gaussian smoothing filter template to process the smoothing of experimental data in the GUKF algorithm, and then the filtering algorithm is used to obtain higher positioning accuracy. This paper utilises simulations and actual experiments to verify and analyse the GUKF algorithm, and the actual experiment environment was divided into line-of-sight (LOS) and non-line-of-sight (NLOS) experimental environments. The measured experimental results indicate that in the static test of location tags in LOS and NLOS experimental environments, the root mean square error (RMSE) of the GUKF algorithm is reduced by 15.88% and 14.10%, respectively; in the dynamic test, the RMSE of the GUKF algorithm is reduced by 16.67% and 17.89%, respectively, compared with the unscented Kalman filter algorithm. In addition, the positioning performance evaluation method of the mean error and cumulative distribution function curve also verifies that the GUKF algorithm has a higher positioning accuracy than the UKF, Least Squares, and Time of Arrival algorithms.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12682","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362369","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":"Enhanced performance of secondary surveillance radar system in dense UAV environments using CDMA techniques","authors":"Haonan Chen,&nbsp;Rui Guo,&nbsp;Zengping Chen","doi":"10.1049/rsn2.12680","DOIUrl":"https://doi.org/10.1049/rsn2.12680","url":null,"abstract":"<p>In future battlefield scenarios, the high density of platforms often leads to multiple responses from existing secondary surveillance radar (SSR) systems, causing collision interference of response signals in the time domain. The frame slotted ALOHA (FSA) algorithm originally used by the system cannot ensure a high identification probability and has a long identification time. In response to these problems, this paper explores the problems of current SSR systems under the urgent need for precise multi-target identification in dense environments. It investigates how to integrate code division multiple access (CDMA) technology with the ALOHA algorithm to enhance the system's target identification probability. The authors propose an improved workflow and signal transceiver principles for the SSR system and validate the excellent performance of the SSR system in multi-target identification within dense environments through simulation experiments based on the proposed composite anti-collision algorithm.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362874","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":"3D-TabNetHS: A hyperspectral image classification method based on improved interpretable 3D attentive TabNet","authors":"Ning Li,&nbsp;Daozhi Wei,&nbsp;Shucai Huang,&nbsp;Yong Zhang","doi":"10.1049/rsn2.12678","DOIUrl":"https://doi.org/10.1049/rsn2.12678","url":null,"abstract":"<p>The classification methods for hyperspectral images (HSI) based on decision trees and convolutional neural networks have shown increasing advantages, but these methods often require a large number of labelled samples for learning, which is difficult for HSI, and the interpretability of the network is not high. Therefore, this paper proposes classification methods based on improved attention interpretable table learning (TabNet) named 3D TabNet HSI (3D-TabNetHS) and unsupervised 3D TabNet HSI (U3D-TabNetHS). These methods use sequential attention to select appropriate HSI spatial-spectral features and add a space spectral information extraction (SSE) module composed of a 3D convolutional neural network (3D-CNN) and fully connected layers to the Attention Transformer module in the original TabNet network to extract spatial-spectral soft features. At the same time, unsupervised learning can be used to retrain the 3D-TabNetHS network, and the classification accuracy of the resulting U3D-TabNetHS network can be further improved. Compared with other HSI classification methods based on decision trees, the HSI classification accuracy of 3D-TabNetHS is higher. On three typical HSI datasets, the accuracy metric overall accuracy of 3D-TabNetHS reached as high as 98.71%, 94.73%, and 97.23%, respectively. Simultaneously, the consistency evaluation metric Kappa also reached 98.56%, 93.98%, and 96.31% respectively. The experimental results indicate the feasibility and reliability of the proposed method in HSI classification.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2749-2767"},"PeriodicalIF":1.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12678","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252449","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}