Iet Radar Sonar and Navigation最新文献

{"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}

{"title":"Spectrum-sensing algorithm based on graph feature fusion","authors":"Shanshan Wu,&nbsp;Guobing Hu,&nbsp;Bin Gu","doi":"10.1049/rsn2.12674","DOIUrl":"https://doi.org/10.1049/rsn2.12674","url":null,"abstract":"<p>Graph-based spectrum sensing in noisy environments has major implications for civilian and military signal processing applications. However, existing algorithms suffer from high computational complexity and performance deterioration at low signal-to-noise ratios (SNRs). Therefore, a spectrum-sensing algorithm based on graph feature fusion using a quadratic form derived from self-loop weights and the graph Laplacian matrix is proposed in this study. The sum of the first and second block maxima of the power spectrum of the observed signal is selected as the input to the graph converter. Self-loop weights are combined with the Laplacian matrix to construct the graph quadratic form, which serves as the test statistic for decision-making. By applying majorisation and the extreme value theory, it is demonstrated that the proposed algorithm outperforms existing methods. The simulation results confirm the robust spectrum-sensing performance across various signal modulation types and pulse shapes. Thus, compared to existing algorithms, except block range- and energy-detection-based methods, the proposed algorithm demonstrates the best spectrum-sensing performance under low SNRs and channel-fading conditions, while achieving the lowest computational complexity. The proposed approach enables more efficient and accurate spectrum sensing, fostering advancements in communication technologies and defence applications.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2709-2725"},"PeriodicalIF":1.4,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12674","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143251972","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":"Robust design of mismatched filters in echo truncation for inter-pulse waveform diversity radar","authors":"Xiaojian Xu,&nbsp;Dehua Zhao,&nbsp;Liang Zhang","doi":"10.1049/rsn2.12684","DOIUrl":"https://doi.org/10.1049/rsn2.12684","url":null,"abstract":"<p>Range sidelobe modulation is a primary issue that forbids application of inter-pulse and intra-coherent processing interval (coherent processing interval) waveform diversity in moving target indication or moving target detection radars. Previous research has adopted mismatched filters (MMFs) to reconstruct identical compressed outputs based on an ideal model. In practice however, echo truncation is inevitable due to transceiver isolation, significantly degrading the effectiveness of MMFs. In this paper, the design of MMFs under an echo-truncated model to achieve a robust design is considered. Specifically, the authors extract the dominant scattering profile from the close-range echo and incorporate it into the MMF design model. To judiciously combine the robust design with the ideal one, a segmented pulse compression scheme is proposed. Numerical results indicate that the proposed robust processing scheme can effectively reduce clutter leakage in both truncated and intact scenarios.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2775-2787"},"PeriodicalIF":1.4,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12684","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143251973","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":"Single receiver Long-Term Evolution passive radar system using signal reconstruction for moving target detection","authors":"Lionel de Guenin,&nbsp;Patrick Rosson,&nbsp;Nicolas Petrochilos,&nbsp;Eric Moreau","doi":"10.1049/rsn2.12662","DOIUrl":"https://doi.org/10.1049/rsn2.12662","url":null,"abstract":"<p>This paper presents a single-antenna receiver passive radar system in the context of moving target detection such as trains, car, planes and UAVs, leveraging the long-term evolution (LTE) network as an illumination source. The proposed system uses signal reconstruction enabled by the telecom structure of the opportune signal in order to forego the use of a reference antenna. This presents the advantage of not relying on a physical signal for reference and its possible defect, potentially yielding better performances. The techniques introduced are validated through simulation and experiments. Moreover, a simplified passive radar system emphasises one of the key advantage of passive radar over other competing technologies for moving target detection: stealthiness and cost-effectiveness.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2400-2413"},"PeriodicalIF":1.4,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12662","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143251974","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":"Noncircular coherent signal direction of arrival estimation for coprime array: A subspace-based interpolation approach","authors":"Zihan Shen,&nbsp;Hao Hu,&nbsp;Jiaqi Li,&nbsp;Xiaofei Zhang","doi":"10.1049/rsn2.12675","DOIUrl":"https://doi.org/10.1049/rsn2.12675","url":null,"abstract":"<p>To address the array aperture loss caused by mainstream direction of arrival (DOA) estimation algorithms for coherent signals using matrix interpolation techniques, a non-circular (NC) coherent signal direction-finding method that fully utilises covariance matrix information is proposed. The received data is firstly extended by leveraging its NC properties. Then, eigenvalue decomposition is performed on the covariance matrix to extract the signal subspace, which is mapped to a uniform linear array signal subspace via a mapping matrix. The first row of the covariance matrix corresponding to the signal subspace is employed to construct a Toeplitz matrix, and nuclear norm minimisation method is applied to recover the missing information. Finally, to avoid extra NC phase searching, the reduced-dimension method is applied to obtain the estimation results. Performance analysis and simulation results show that the proposed algorithm achieves improvements in computational complexity, source estimation capability, and estimation accuracy.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2726-2736"},"PeriodicalIF":1.4,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248946","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":"Multi-target cognitive electronic reconnaissance for unmanned aerial vehicles based on scene reconstruction","authors":"Yun Zhang,&nbsp;Shixun You,&nbsp;Yunbin Yan,&nbsp;Qiaofeng Ou,&nbsp;Jie Liu,&nbsp;Ling Chen,&nbsp;Xiang Zhu","doi":"10.1049/rsn2.12668","DOIUrl":"https://doi.org/10.1049/rsn2.12668","url":null,"abstract":"<p>Model-free deep reinforcement learning (DRL) is regarded as an effective approach for multi-target cognitive electronic reconnaissance (MCER) missions. However, DRL networks with poor generalisation can significantly reduce mission completion rates when parameters such as reconnaissance area size, target number, and platform speed vary slightly. To address this issue, this paper introduces a novel scene reconstruction method for MCER missions and a mission group adaptive transfer deep reinforcement learning (MTDRL) algorithm. The algorithm enables quick adaptation of reconnaissance strategies for varied mission scenes by transferring strategy templates and compressing multi-target perception states. To validate the method, the authors developed a transfer learning model for unmanned aerial vehicle (UAV) MCER. Three sets of experiments are conducted by varying the reconnaissance area size, the target number, and the platform speed. The results show that the MTDRL algorithm outperforms two commonly used DRL algorithms, with an 18% increase in mission completion rate and a 5.49 h reduction in training time. Furthermore, the mission completion rate of the MTDRL algorithm is much higher than that of a typical non-DRL algorithm. The UAV demonstrates stable hovering and repeat reconnaissance behaviours at the radar detection boundary, ensuring flight safety during missions.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2667-2680"},"PeriodicalIF":1.4,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248784","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":"Near-field scattering centre modelling for complex targets","authors":"Yanxi Chen,&nbsp;Kunyi Guo,&nbsp;Zhouyang Liu,&nbsp;Xinqing Sheng","doi":"10.1049/rsn2.12667","DOIUrl":"https://doi.org/10.1049/rsn2.12667","url":null,"abstract":"<p>The parametric scattering centre (SC) model of the target has been widely used in the rapid simulation of radar image. Most of the existing SC models are suitable for far-field scattering where the radar cross section (RCS) is independent of the distance from the target to the radar. However, when the electromagnetic scattering of the target is in the near-field region of radiation, the RCS of the target changes dramatically in distance. The existing SC model cannot characterise these important near-field characteristics. In this paper, the near-field model with the dependence description of the SC on the distance is proposed. Firstly, the scattering characteristics of several main geometric structures of the extended target in the near-field radiation region are analysed. Then, on the basis of far-field SC model expressions, the description of the relationship between amplitude and phase of the SC model and distance is added. Finally, the correctness of the near-field SC model is verified by comparing with the accurate full-wave numerical method. In order to further illustrate the practicability of the near-field SC model, an actual aircraft target is used. The results proved that the near-field SC model has the advantages of high accuracy and high computational efficiency.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2657-2666"},"PeriodicalIF":1.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12667","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248362","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}