Advancement toward fully autonomous systems requires enhanced sensing and perception, particularly a 360° vision for safe maneuvering. One approach to achieving this is through a distributed network of radar sensors, operating in homogeneous or heterogeneous configurations, strategically positioned to provide increased coverage and visibility in otherwise blind regions. Such a multiperspective sensing network, complemented with multimodal signal processing, can significantly improve the angular resolution of the radar, delivering high-fidelity scene imagery essential for region classification and path planning. This study presents a methodology for multimodal and multiperspective sensing using heterogeneous radar sensors, utilizing Doppler beam sharpening (DBS) within multiple-input-multiple-output (MIMO) radars to enhance the resolution and coverage. Traditional frequency-modulated continuous wave (FMCW)–MIMO radars, currently the most widely used configuration, are prone to Doppler aliasing, limiting the field of view (FoV) in DBS and MIMO–DBS processing. To address this limitation, the effective FoV in multiperspective image is extended to that provided by the radar’s physical aperture. The proposed framework is validated using 77-GHz radar chipsets in both automotive and maritime conditions, with sensors mounted in front-looking, corner-looking, and side-looking orientations.
{"title":"High-Resolution Augmented Multimodal Sensing of Distributed Radar Network","authors":"Anum Pirkani;Dillon Kumar;Edward Hoare;Muge Bekar;Natalie Reeves;Mikhail Cherniakov;Marina Gashinova","doi":"10.1109/TRS.2025.3581396","DOIUrl":"https://doi.org/10.1109/TRS.2025.3581396","url":null,"abstract":"Advancement toward fully autonomous systems requires enhanced sensing and perception, particularly a 360° vision for safe maneuvering. One approach to achieving this is through a distributed network of radar sensors, operating in homogeneous or heterogeneous configurations, strategically positioned to provide increased coverage and visibility in otherwise blind regions. Such a multiperspective sensing network, complemented with multimodal signal processing, can significantly improve the angular resolution of the radar, delivering high-fidelity scene imagery essential for region classification and path planning. This study presents a methodology for multimodal and multiperspective sensing using heterogeneous radar sensors, utilizing Doppler beam sharpening (DBS) within multiple-input-multiple-output (MIMO) radars to enhance the resolution and coverage. Traditional frequency-modulated continuous wave (FMCW)–MIMO radars, currently the most widely used configuration, are prone to Doppler aliasing, limiting the field of view (FoV) in DBS and MIMO–DBS processing. To address this limitation, the effective FoV in multiperspective image is extended to that provided by the radar’s physical aperture. The proposed framework is validated using 77-GHz radar chipsets in both automotive and maritime conditions, with sensors mounted in front-looking, corner-looking, and side-looking orientations.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"905-918"},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The objective of zero-shot synthetic aperture radar (SAR) image target recognition is to identify the novel unobserved targets for which no training samples are available. The zero-shot recognition method for SAR targets merits investigation, where using electromagnetic simulated images as training data is a viable approach. Nevertheless, the networks trained on the simulated images exhibit difficulty in generalizing to the real images due to the inherent discrepancies in the distribution of the simulated and the real domains. The majority of existing research employs unsupervised domain adaptation methods to address such cross-domain recognition problems. However, these methods are not applicable in zero-shot scenarios, as they require the availability of unlabeled real data from unknown classes during training. Therefore, to address the challenging issue of zero-shot cross-domain recognition for SAR targets, a zero-shot domain adaptation (ZSDA) for SAR target recognition based on cooperative learning of domain alignment and task alignment is proposed. Specifically, we perform domain adaptation using the simulated and real data from the seen classes, to ensure that this alignment can be generalized to the unseen classes. First, a transfer-weighted domain adversarial learning method is proposed to achieve a more robust domain alignment of the seen classes. Second, a classification-based adversarial learning method is proposed to achieve task alignment between the seen and unseen classes within two domains. Finally, a feature fusion refinement module is proposed for the cooperative learning of the two alignment processes. In the context of collaborative learning, task alignment facilitates the transfer of the domain alignment learned from the seen classes to the unseen classes. The experimental results demonstrate the efficacy of the proposed method in SAR zero-shot cross-domain recognition, achieving recognition accuracies of 91.68%, 85.83%, 83.90%, and 77.73% for three unseen class real images across four distinct experimental groups, surpassing the current state-of-the-art methods.
{"title":"Zero-Shot Domain Adaptation for SAR Target Recognition Based on Cooperative Learning of Domain Alignment and Task Alignment","authors":"Guo Chen;Siqian Zhang;Zheng Zhou;Lingjun Zhao;Gangyao Kuang","doi":"10.1109/TRS.2025.3580543","DOIUrl":"https://doi.org/10.1109/TRS.2025.3580543","url":null,"abstract":"The objective of zero-shot synthetic aperture radar (SAR) image target recognition is to identify the novel unobserved targets for which no training samples are available. The zero-shot recognition method for SAR targets merits investigation, where using electromagnetic simulated images as training data is a viable approach. Nevertheless, the networks trained on the simulated images exhibit difficulty in generalizing to the real images due to the inherent discrepancies in the distribution of the simulated and the real domains. The majority of existing research employs unsupervised domain adaptation methods to address such cross-domain recognition problems. However, these methods are not applicable in zero-shot scenarios, as they require the availability of unlabeled real data from unknown classes during training. Therefore, to address the challenging issue of zero-shot cross-domain recognition for SAR targets, a zero-shot domain adaptation (ZSDA) for SAR target recognition based on cooperative learning of domain alignment and task alignment is proposed. Specifically, we perform domain adaptation using the simulated and real data from the seen classes, to ensure that this alignment can be generalized to the unseen classes. First, a transfer-weighted domain adversarial learning method is proposed to achieve a more robust domain alignment of the seen classes. Second, a classification-based adversarial learning method is proposed to achieve task alignment between the seen and unseen classes within two domains. Finally, a feature fusion refinement module is proposed for the cooperative learning of the two alignment processes. In the context of collaborative learning, task alignment facilitates the transfer of the domain alignment learned from the seen classes to the unseen classes. The experimental results demonstrate the efficacy of the proposed method in SAR zero-shot cross-domain recognition, achieving recognition accuracies of 91.68%, 85.83%, 83.90%, and 77.73% for three unseen class real images across four distinct experimental groups, surpassing the current state-of-the-art methods.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"890-904"},"PeriodicalIF":0.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-17DOI: 10.1109/TRS.2025.3580623
Hajar Abedi;Jenna Hall;Ji Beom Bae;Plinio P. Morita;Alexander Wong;Jennifer Boger;George Shaker
Gait analysis is one of the most useful predictors of disease in older adults, but it is not always practical for physicians to monitor. This article aimed to create a system that could continuously and reliably monitor gait patterns of varying step lengths and speeds in cluttered environments, enabling around-the-clock monitoring in personal living spaces. This novel study uses multiple input multiple output frequency-modulated continuous-wave (MIMO FMCW) radar to track nonlinear movement in cluttered environments designed to replicate a living space in a home. A subjects tracker and association (STA) algorithm was proposed to distinguish direct signals with multipath effects and remove ghost signals created by clutter. Six participants were instructed to walk along designated paths with varied step lengths (30, 60, and 80 cm), and our findings supported the system’s ability to capture walking speed, step count, and step length. The system was successful in accurately tracking gait parameters in naturalistic settings, offering a potential solution to autonomous, continuous in-home gait analysis.
{"title":"Continuous In-Home Gait Analysis Using FMCW Radar in Naturalistic Environments","authors":"Hajar Abedi;Jenna Hall;Ji Beom Bae;Plinio P. Morita;Alexander Wong;Jennifer Boger;George Shaker","doi":"10.1109/TRS.2025.3580623","DOIUrl":"https://doi.org/10.1109/TRS.2025.3580623","url":null,"abstract":"Gait analysis is one of the most useful predictors of disease in older adults, but it is not always practical for physicians to monitor. This article aimed to create a system that could continuously and reliably monitor gait patterns of varying step lengths and speeds in cluttered environments, enabling around-the-clock monitoring in personal living spaces. This novel study uses multiple input multiple output frequency-modulated continuous-wave (MIMO FMCW) radar to track nonlinear movement in cluttered environments designed to replicate a living space in a home. A subjects tracker and association (STA) algorithm was proposed to distinguish direct signals with multipath effects and remove ghost signals created by clutter. Six participants were instructed to walk along designated paths with varied step lengths (30, 60, and 80 cm), and our findings supported the system’s ability to capture walking speed, step count, and step length. The system was successful in accurately tracking gait parameters in naturalistic settings, offering a potential solution to autonomous, continuous in-home gait analysis.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"969-981"},"PeriodicalIF":0.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-17DOI: 10.1109/TRS.2025.3580606
Xiang Wang;Yumiao Wang;Guolong Cui
Maritime radar detectors developed using deep learning technology have demonstrated promising performance in the clutter environment. However, real clutter environments are usually time-varying, and the nonstationary radar data stream easily breaks the independent and identically distributed (i.i.d.) prerequisite of standard deep learning detectors, decreasing the detector’s performance. This article considers the problem of adaptive maritime radar target detection for deep learning-based detectors in time-varying clutter environments. We propose an adaptive target detection framework based on an active self-learning (SL) strategy, which can actively sense the environment shift and update the detector parameters correspondingly through SL. Specifically, we first use the annotated dataset to train an initial detector. Then, we design an environment sensing module by adding a subdetection head on the detector. When the detector works in time-varying clutter environments, the entropy between the detector’s output and the subdetection head’s output is utilized to sense the environment shift. Next, we propose an SL strategy that combines adaptive pseudo-label generation with consistency regularization. Once the environment shift is detected, the detector parameters are updated by the proposed SL strategy, improving the detector’s performance in time-varying clutter environments. Experimental results on the public maritime radar database validate the effectiveness of the proposed framework.
{"title":"Adaptive Intelligent Radar Target Detection in Time-Varying Sea Clutter via Activate Self-Learning","authors":"Xiang Wang;Yumiao Wang;Guolong Cui","doi":"10.1109/TRS.2025.3580606","DOIUrl":"https://doi.org/10.1109/TRS.2025.3580606","url":null,"abstract":"Maritime radar detectors developed using deep learning technology have demonstrated promising performance in the clutter environment. However, real clutter environments are usually time-varying, and the nonstationary radar data stream easily breaks the independent and identically distributed (i.i.d.) prerequisite of standard deep learning detectors, decreasing the detector’s performance. This article considers the problem of adaptive maritime radar target detection for deep learning-based detectors in time-varying clutter environments. We propose an adaptive target detection framework based on an active self-learning (SL) strategy, which can actively sense the environment shift and update the detector parameters correspondingly through SL. Specifically, we first use the annotated dataset to train an initial detector. Then, we design an environment sensing module by adding a subdetection head on the detector. When the detector works in time-varying clutter environments, the entropy between the detector’s output and the subdetection head’s output is utilized to sense the environment shift. Next, we propose an SL strategy that combines adaptive pseudo-label generation with consistency regularization. Once the environment shift is detected, the detector parameters are updated by the proposed SL strategy, improving the detector’s performance in time-varying clutter environments. Experimental results on the public maritime radar database validate the effectiveness of the proposed framework.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"919-934"},"PeriodicalIF":0.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At the current state of the scientific discourse on quantum radar, the best understood and experimentally feasible types of implementation are based on two-mode-squeezed-vacuum (TMSV) photon states and aimed at the task of target detection. The operating environment, in which an advantage over classical radar may be attainable, is therefore limited to the extreme regimes of very low signal-to-noise ratios (SNRs) and high thermal noise levels as well as confining the required hardware at mK temperatures. In this work, we approach the open question of how to optimally operate a potential quantum radar system. To this end, we define the optimal operation using the detection advantage against classical radar as well as the efficient usage of the resource measurement time. We show that there is a tradeoff between time efficiency and outperformance of classical radar and specify the conditions for such an operation. Building on this aspect, we investigate the concept of the fair classical comparison to facilitate the understanding of its relation to quantum radar.
{"title":"A Note on the Efficient Operation of Quantum Radar and the Fair Classical Comparison","authors":"Florian Bischeltsrieder;Michael Würth;Markus Peichl;Wolfgang Utschick","doi":"10.1109/TRS.2025.3579042","DOIUrl":"https://doi.org/10.1109/TRS.2025.3579042","url":null,"abstract":"At the current state of the scientific discourse on quantum radar, the best understood and experimentally feasible types of implementation are based on two-mode-squeezed-vacuum (TMSV) photon states and aimed at the task of target detection. The operating environment, in which an advantage over classical radar may be attainable, is therefore limited to the extreme regimes of very low signal-to-noise ratios (SNRs) and high thermal noise levels as well as confining the required hardware at mK temperatures. In this work, we approach the open question of how to optimally operate a potential quantum radar system. To this end, we define the optimal operation using the detection advantage against classical radar as well as the efficient usage of the resource measurement time. We show that there is a tradeoff between time efficiency and outperformance of classical radar and specify the conditions for such an operation. Building on this aspect, we investigate the concept of the fair classical comparison to facilitate the understanding of its relation to quantum radar.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"875-880"},"PeriodicalIF":0.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11032128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-12DOI: 10.1109/TRS.2025.3579026
Marc Reinecke;Theresa Noegel;Oliver Sura;Marcel Hoffmann;Peter Gulden;Martin Vossiek
Automotive forward-looking synthetic aperture radar (FL-SAR) has recently attracted research attention, not only for the resolution gain but also for the exceptional signal-to-clutter ratios (SCRs) that can be achieved. However, when utilizing the backprojection (BP) algorithm for FL-SAR, a mirror-target problem emerges, which is attributable to an inherent flaw of image reconstruction with 2-D spatial sampling grids, such as the ones created in FL-SAR. Constructive superposition of ambiguous subapertures produces magnitudes, which can be significantly higher than those of real targets. This causes false detections and severely impacts higher level tasks such as trajectory planning. This article aims to describe the phenomenon of mirror targets using the well-known example of the BP algorithm. Based on a thorough understanding of the undesirable artifacts, four suppression methods to mitigate false detections were developed. Their viability was ensured through simulative tests. Experimental evaluation in real-world measurement scenarios proved the effectiveness and robustness of all methods. A phase coherency-based classification approach yielded the most accurate results by detecting mirror-target-specific features in the images, thereby enhancing FL-SAR’s imaging capabilities.
{"title":"Mitigation of Mirror Targets in Automotive Forward-Looking Synthetic Aperture Radar","authors":"Marc Reinecke;Theresa Noegel;Oliver Sura;Marcel Hoffmann;Peter Gulden;Martin Vossiek","doi":"10.1109/TRS.2025.3579026","DOIUrl":"https://doi.org/10.1109/TRS.2025.3579026","url":null,"abstract":"Automotive forward-looking synthetic aperture radar (FL-SAR) has recently attracted research attention, not only for the resolution gain but also for the exceptional signal-to-clutter ratios (SCRs) that can be achieved. However, when utilizing the backprojection (BP) algorithm for FL-SAR, a mirror-target problem emerges, which is attributable to an inherent flaw of image reconstruction with 2-D spatial sampling grids, such as the ones created in FL-SAR. Constructive superposition of ambiguous subapertures produces magnitudes, which can be significantly higher than those of real targets. This causes false detections and severely impacts higher level tasks such as trajectory planning. This article aims to describe the phenomenon of mirror targets using the well-known example of the BP algorithm. Based on a thorough understanding of the undesirable artifacts, four suppression methods to mitigate false detections were developed. Their viability was ensured through simulative tests. Experimental evaluation in real-world measurement scenarios proved the effectiveness and robustness of all methods. A phase coherency-based classification approach yielded the most accurate results by detecting mirror-target-specific features in the images, thereby enhancing FL-SAR’s imaging capabilities.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"982-994"},"PeriodicalIF":0.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-06DOI: 10.1109/TRS.2025.3577535
Gavriel B. Aminov;Zeev Zalevsky
Accurate extraction of radar cross section (RCS) from real-world measurements is challenged by noise, clutter, and near-field effects. This article presents the dictionary pursuit (DP) method, a framework based on compressive sensing that addresses these issues. The DP method employs distinct image and range-Doppler bases to simultaneously represent the target and environmental contamination within a single, weighted L1-norm optimization. Building upon previous concepts explored by other authors, this work provides a detailed mathematical formulation, algorithmic improvements, and a comprehensive workflow for this multibasis approach. Through validation with both numerical simulations and experimental measurements, we demonstrate the DP method’s enhanced performance compared to classical Fourier-based techniques. It automatically separates the target signature from clutter and noise, achieves a more accurate near-field to far-field transformation (NFFFT) by avoiding approximate inverse transforms, and inherently yields a higher quality super-resolved inverse synthetic aperture radar (ISAR) image with reduced sidelobes.
{"title":"Automated RCS Measurements Processing via Multibasis Dictionary and Compressive Sensing","authors":"Gavriel B. Aminov;Zeev Zalevsky","doi":"10.1109/TRS.2025.3577535","DOIUrl":"https://doi.org/10.1109/TRS.2025.3577535","url":null,"abstract":"Accurate extraction of radar cross section (RCS) from real-world measurements is challenged by noise, clutter, and near-field effects. This article presents the dictionary pursuit (DP) method, a framework based on compressive sensing that addresses these issues. The DP method employs distinct image and range-Doppler bases to simultaneously represent the target and environmental contamination within a single, weighted L1-norm optimization. Building upon previous concepts explored by other authors, this work provides a detailed mathematical formulation, algorithmic improvements, and a comprehensive workflow for this multibasis approach. Through validation with both numerical simulations and experimental measurements, we demonstrate the DP method’s enhanced performance compared to classical Fourier-based techniques. It automatically separates the target signature from clutter and noise, achieves a more accurate near-field to far-field transformation (NFFFT) by avoiding approximate inverse transforms, and inherently yields a higher quality super-resolved inverse synthetic aperture radar (ISAR) image with reduced sidelobes.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1207-1220"},"PeriodicalIF":0.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-03DOI: 10.1109/TRS.2025.3575786
David Luong;Ian W. K. Lam;Sreeraman Rajan
Noise radars have the same mathematical description as a type of quantum radar known as quantum two-mode squeezing radar (QTMS radar). Although their physical implementations are very different, this mathematical similarity allows us to analyze them collectively, for which reason we call them noise-type radars. The target detection performance of noise-type radars depend crucially on a parameter called the “correlation coefficient.” In this article, we show that the correlation coefficient reduces to the signal-to-noise ratio (SNR) in certain limiting cases when the SNR is appropriately defined. This allows us to translate between the correlation coefficient, which is the more fundamental parameter in the theory of noise-type radars, and the SNR, which is more familiar to engineers. To illustrate the ideas in this article, we present experimental results from a laboratory-based noise radar.
{"title":"Noise-Type Radars With Gaussian Statistics: SNR and the Correlation Coefficient","authors":"David Luong;Ian W. K. Lam;Sreeraman Rajan","doi":"10.1109/TRS.2025.3575786","DOIUrl":"https://doi.org/10.1109/TRS.2025.3575786","url":null,"abstract":"Noise radars have the same mathematical description as a type of quantum radar known as quantum two-mode squeezing radar (QTMS radar). Although their physical implementations are very different, this mathematical similarity allows us to analyze them collectively, for which reason we call them noise-type radars. The target detection performance of noise-type radars depend crucially on a parameter called the “correlation coefficient.” In this article, we show that the correlation coefficient reduces to the signal-to-noise ratio (SNR) in certain limiting cases when the SNR is appropriately defined. This allows us to translate between the correlation coefficient, which is the more fundamental parameter in the theory of noise-type radars, and the SNR, which is more familiar to engineers. To illustrate the ideas in this article, we present experimental results from a laboratory-based noise radar.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"881-889"},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-02DOI: 10.1109/TRS.2025.3575479
Youmin Qu;Xingpeng Mao;Zhibo Tang;Yiming Wang
To enhance the maneuverability and extend the detection range of high-frequency surface wave radar (HFSWR), shipborne systems have been developed as an alternative to shore-based platforms. However, the limited space on shipborne platforms results in a small radar array aperture, which consequently diminishes the radar’s direction-of-arrival (DOA) estimation performance. Additionally, the target echoes received by HFSWR are often accompanied by a large amount of strong clutter. Traditional extrapolation-based aperture extension methods fail because they cannot effectively distinguish between targets and clutter. Therefore, how to extend the aperture of shipborne HFSWR remains a problem to be addressed. To overcome these challenges, we improved conventional extrapolation-based aperture extension techniques by incorporating the signal processing workflow of HFSWR and proposed a novel aperture extension method for uniform linear arrays, based on range-Doppler domain spatiotemporal data block extrapolation (RDSDBE). Specifically, on the one hand, we extend the array aperture in the range-Doppler (RD) domain to address the failure of traditional aperture extension methods in the presence of strong clutter. On the other hand, we segment the target echoes in the time domain to tackle the issue of large aperture extension errors caused by the limited number of snapshots in shipborne scenarios. Through simulation and experimental data, we validated the proposed RDSDBE method and analyzed its performance.
{"title":"A Virtual Aperture Extension Method for Shipborne HFSWR Based on RD-Domain Spatiotemporal Data Block Extrapolation","authors":"Youmin Qu;Xingpeng Mao;Zhibo Tang;Yiming Wang","doi":"10.1109/TRS.2025.3575479","DOIUrl":"https://doi.org/10.1109/TRS.2025.3575479","url":null,"abstract":"To enhance the maneuverability and extend the detection range of high-frequency surface wave radar (HFSWR), shipborne systems have been developed as an alternative to shore-based platforms. However, the limited space on shipborne platforms results in a small radar array aperture, which consequently diminishes the radar’s direction-of-arrival (DOA) estimation performance. Additionally, the target echoes received by HFSWR are often accompanied by a large amount of strong clutter. Traditional extrapolation-based aperture extension methods fail because they cannot effectively distinguish between targets and clutter. Therefore, how to extend the aperture of shipborne HFSWR remains a problem to be addressed. To overcome these challenges, we improved conventional extrapolation-based aperture extension techniques by incorporating the signal processing workflow of HFSWR and proposed a novel aperture extension method for uniform linear arrays, based on range-Doppler domain spatiotemporal data block extrapolation (RDSDBE). Specifically, on the one hand, we extend the array aperture in the range-Doppler (RD) domain to address the failure of traditional aperture extension methods in the presence of strong clutter. On the other hand, we segment the target echoes in the time domain to tackle the issue of large aperture extension errors caused by the limited number of snapshots in shipborne scenarios. Through simulation and experimental data, we validated the proposed RDSDBE method and analyzed its performance.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"818-831"},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-02DOI: 10.1109/TRS.2025.3575462
Edward V. Semyonov;Maxim A. Nazarov;Kirill M. Poltorykhin;Andrey A. Berezin;Alexey V. Fateev
It is shown that for a typical electronic gadget at test voltage pulses with a duration of ~1 ns and an electric field strength of up to ~200 V/m, the waveform of the nonlinear response from this object can be found from the test pulse by using an impulse response function, the shape of which is almost independent of the amplitude and waveform of the test pulse. The amplitude of the nonlinear object’s response is determined by both the spectral consistency between the test signal and the “test signal to nonlinear response” transfer function (signals with a higher level of high frequencies have an advantage) and by the effect of the test signal on the manifestation of the nonlinear properties of object internal circuits (signals with a higher level of low frequencies have an advantage). It has been demonstrated that the functional characterizing the influence of the test signal on the manifestation of nonlinear object’s properties is described by a quadratic dependence in the amplitude sense and is approximated by a low- pass filter in the frequency sense. By estimating the frequency properties of this filter, a measurement-based estimate of the time constantofobjects under test (about 1 ns) was obtained. On the basis of the above observations, the behavioral models of the testing objects have been synthesized. For ultrawideband pulse signals of various waveforms and amplitudes, these models give an error of no more than 17%.
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