Pub Date : 2025-08-29DOI: 10.1109/TRS.2025.3604214
Xiaodi Li;Yuguan Hou;Zihan Xu;Xinfei Jin;Fulin Su;Hongxu Li
High-resolution range profile (HRRP)-based radar automatic target recognition (RATR) is crucial in capturing target structural characteristics across all-day and all-weather environments. However, existing HRRP-based RATR methods struggle to adapt to diverse target aspects and backscattering characteristics due to insufficient modeling of spatial structures and spectral periodicity. To address these limitations, we propose the MSHP-Net, a multiscale hybrid perception network with granularity decoupling and spectral enhancement for HRRP target recognition. The MSHP-Net employs a multiscale hybrid perception (HP) encoder to jointly capture spatial–spectral domain features by combining spatial feature decoupling and spectral feature recalibration. Specifically, we extract multigranularity spatial features and decouple them into granularity-invariant and granularity-variant components based on an adaptive singular value decomposition (SVD). This process explicitly enhances structural consistency and preserves fine-grained variations, effectively modeling varying target aspects and mitigating structural distortions inherent in HRRP data. To capture global spectral correlations and periodic scattering characteristics, we recalibrate the spectral distribution and apply spectrally enhanced attention, emphasizing the critical spectral bands and suppressing background noise. To promote multiscale hybrid features interaction, we introduce a hierarchical affinity-guided gating to propagate cross-scale relevant information flow, balancing low-level details with high-level semantics for more comprehensive feature representations. Finally, we aggregate scale-wise features and predict the final classification. Comparative experiments on both simulated and measured datasets validate the effectiveness of the proposed network.
{"title":"A Multiscale Hybrid Perception Network With Granularity Decoupling and Spectral Enhancement for HRRP Target Recognition","authors":"Xiaodi Li;Yuguan Hou;Zihan Xu;Xinfei Jin;Fulin Su;Hongxu Li","doi":"10.1109/TRS.2025.3604214","DOIUrl":"https://doi.org/10.1109/TRS.2025.3604214","url":null,"abstract":"High-resolution range profile (HRRP)-based radar automatic target recognition (RATR) is crucial in capturing target structural characteristics across all-day and all-weather environments. However, existing HRRP-based RATR methods struggle to adapt to diverse target aspects and backscattering characteristics due to insufficient modeling of spatial structures and spectral periodicity. To address these limitations, we propose the MSHP-Net, a multiscale hybrid perception network with granularity decoupling and spectral enhancement for HRRP target recognition. The MSHP-Net employs a multiscale hybrid perception (HP) encoder to jointly capture spatial–spectral domain features by combining spatial feature decoupling and spectral feature recalibration. Specifically, we extract multigranularity spatial features and decouple them into granularity-invariant and granularity-variant components based on an adaptive singular value decomposition (SVD). This process explicitly enhances structural consistency and preserves fine-grained variations, effectively modeling varying target aspects and mitigating structural distortions inherent in HRRP data. To capture global spectral correlations and periodic scattering characteristics, we recalibrate the spectral distribution and apply spectrally enhanced attention, emphasizing the critical spectral bands and suppressing background noise. To promote multiscale hybrid features interaction, we introduce a hierarchical affinity-guided gating to propagate cross-scale relevant information flow, balancing low-level details with high-level semantics for more comprehensive feature representations. Finally, we aggregate scale-wise features and predict the final classification. Comparative experiments on both simulated and measured datasets validate the effectiveness of the proposed network.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1183-1194"},"PeriodicalIF":0.0,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997999","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 : 2025-08-28DOI: 10.1109/TRS.2025.3603807
Chun-Yu Hou;Chieh-Chih Wang;Wen-Chieh Lin
This article addresses the challenge of elevation angle estimation in 3-D automotive radar, a critical limitation for achieving accurate and reliable 3-D scene understanding in autonomous driving. While the vertical Doppler beam sharpening (DBS) provides a foundation for height estimation, existing implementations often suffer from limitations due to measurement noise. We enhance DBS using a rigorous uncertainty analysis and a robust, temporal filtering approach. Our analysis reveals the significant impact of target-sensor geometry, particularly small elevation angles, on estimation errors. To mitigate these uncertainties, we develop a simple yet effective method combining an extended Kalman filter (EKF) for temporal filtering with robust data association to reject spurious detections. Real-world experiments on highway and ITRI campus datasets, spanning 34 and 1.9 km, respectively, using a standard 3-D radar and a prebuilt LiDAR map for ground truth, demonstrate a substantial improvement in height accuracy. Compared with unfiltered DBS, our method increases height accuracy within 1 m from 53.41% to 62.32% on the highway and from 47.74% to 57.56% on the ITRI campus.
{"title":"Improving Height Estimation for Stationary Targets With 3-D Automotive Radar: From Uncertainty Analysis to Temporal Filtering","authors":"Chun-Yu Hou;Chieh-Chih Wang;Wen-Chieh Lin","doi":"10.1109/TRS.2025.3603807","DOIUrl":"https://doi.org/10.1109/TRS.2025.3603807","url":null,"abstract":"This article addresses the challenge of elevation angle estimation in 3-D automotive radar, a critical limitation for achieving accurate and reliable 3-D scene understanding in autonomous driving. While the vertical Doppler beam sharpening (DBS) provides a foundation for height estimation, existing implementations often suffer from limitations due to measurement noise. We enhance DBS using a rigorous uncertainty analysis and a robust, temporal filtering approach. Our analysis reveals the significant impact of target-sensor geometry, particularly small elevation angles, on estimation errors. To mitigate these uncertainties, we develop a simple yet effective method combining an extended Kalman filter (EKF) for temporal filtering with robust data association to reject spurious detections. Real-world experiments on highway and ITRI campus datasets, spanning 34 and 1.9 km, respectively, using a standard 3-D radar and a prebuilt LiDAR map for ground truth, demonstrate a substantial improvement in height accuracy. Compared with unfiltered DBS, our method increases height accuracy within 1 m from 53.41% to 62.32% on the highway and from 47.74% to 57.56% on the ITRI campus.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1195-1206"},"PeriodicalIF":0.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998000","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 : 2025-08-25DOI: 10.1109/TRS.2025.3602070
Yu Rong;Isabella Lenz;Drake Silbernagel;Adarsh A. Venkataramani;Daniel W. Bliss
This article presents the development and application of high-resolution radar-based imaging for human morphological analysis, specifically focusing on enhancing vital signs’ detection using millimeter-wave (mmWave) radar technology. The authors use advanced 3-D radar imaging techniques, using multielement uniform linear transmit and receive antenna arrays, to construct detailed grids of radar backscatter points in space. Implementing multi-input multi-output (MIMO) beamforming and motion-enhanced imaging techniques addresses key challenges such as static environmental clutter, multipath interference, and sidelobe effects, which degrade imaging quality. This study introduces a novel approach to vital signs’ analysis by constructing a vital signs’ intensity map, which displays the distribution of respiration and pulse sensitivity across the human chest. This method enables precise body localization for targeted vital signs’ monitoring. The system’s capabilities are validated through both simulation and real-world experiments, demonstrating its effectiveness in various scenarios, including multipoint respiration monitoring, multisubject imaging, and enhanced heartbeat detection through localized measurement. The results of this study highlight the potential of mmWave radar technology for contactless health monitoring, offering significant improvements over traditional radar-based methods in terms of accuracy and spatial resolution. The advanced imaging capabilities developed in this research pave the way for innovative applications in healthcare and human performance monitoring, providing a promising tool for noninvasive vital signs’ sensing.
{"title":"mmWaveCam: Human Vital Motion Imaging and Focused Vital Signs’ Sensing","authors":"Yu Rong;Isabella Lenz;Drake Silbernagel;Adarsh A. Venkataramani;Daniel W. Bliss","doi":"10.1109/TRS.2025.3602070","DOIUrl":"https://doi.org/10.1109/TRS.2025.3602070","url":null,"abstract":"This article presents the development and application of high-resolution radar-based imaging for human morphological analysis, specifically focusing on enhancing vital signs’ detection using millimeter-wave (mmWave) radar technology. The authors use advanced 3-D radar imaging techniques, using multielement uniform linear transmit and receive antenna arrays, to construct detailed grids of radar backscatter points in space. Implementing multi-input multi-output (MIMO) beamforming and motion-enhanced imaging techniques addresses key challenges such as static environmental clutter, multipath interference, and sidelobe effects, which degrade imaging quality. This study introduces a novel approach to vital signs’ analysis by constructing a vital signs’ intensity map, which displays the distribution of respiration and pulse sensitivity across the human chest. This method enables precise body localization for targeted vital signs’ monitoring. The system’s capabilities are validated through both simulation and real-world experiments, demonstrating its effectiveness in various scenarios, including multipoint respiration monitoring, multisubject imaging, and enhanced heartbeat detection through localized measurement. The results of this study highlight the potential of mmWave radar technology for contactless health monitoring, offering significant improvements over traditional radar-based methods in terms of accuracy and spatial resolution. The advanced imaging capabilities developed in this research pave the way for innovative applications in healthcare and human performance monitoring, providing a promising tool for noninvasive vital signs’ sensing.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1221-1232"},"PeriodicalIF":0.0,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027899","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}
Ground-penetrating radar (GPR) localization has gained increasing attention in autonomous driving due to its resilience against surface appearance and weather conditions. However, existing localization using GPR (LGPR) methods based on single-scan matching often suffer from limited sensing range and frequent mismatches, particularly in the absence of complementary sensors. To address these challenges, we propose the first dedicated sequence-based matching framework for LGPR localization, which systematically exploits temporal continuity for robust place recognition. The proposed framework comprises four key components: a learning-based pretraining module to suppress weather-induced variations in GPR signatures; an information–theoretic correlation method for estimating lateral deviations; a velocity-constrained search strategy to enforce spatial consistency during coarse alignment; and a reranking mechanism to refine the final matching outcome. Unlike existing methods that focus solely on descriptor design, our framework is modular, extensible, and emphasizes the sequential nature of localization. It supports both handcrafted and learning-based GPR features, enabling fair and reproducible comparisons across different extraction pipelines. Experiments on both public and in-house datasets show that our method significantly improves localization accuracy, validating its robustness, adaptability, and the critical role of sequence modeling in LGPR-based localization.
{"title":"SeqLGPR: A Sequential Subsurface Feature-Based Framework for Vehicle Place Recognition","authors":"Pengyu Zhang;Shuaifeng Zhi;Xieyuanli Chen;Beizhen Bi;Zhuo Xu;Yuwei Chen;Liang Shen;Tian Jin;Xiaotao Huang","doi":"10.1109/TRS.2025.3599141","DOIUrl":"https://doi.org/10.1109/TRS.2025.3599141","url":null,"abstract":"Ground-penetrating radar (GPR) localization has gained increasing attention in autonomous driving due to its resilience against surface appearance and weather conditions. However, existing localization using GPR (LGPR) methods based on single-scan matching often suffer from limited sensing range and frequent mismatches, particularly in the absence of complementary sensors. To address these challenges, we propose the first dedicated sequence-based matching framework for LGPR localization, which systematically exploits temporal continuity for robust place recognition. The proposed framework comprises four key components: a learning-based pretraining module to suppress weather-induced variations in GPR signatures; an information–theoretic correlation method for estimating lateral deviations; a velocity-constrained search strategy to enforce spatial consistency during coarse alignment; and a reranking mechanism to refine the final matching outcome. Unlike existing methods that focus solely on descriptor design, our framework is modular, extensible, and emphasizes the sequential nature of localization. It supports both handcrafted and learning-based GPR features, enabling fair and reproducible comparisons across different extraction pipelines. Experiments on both public and in-house datasets show that our method significantly improves localization accuracy, validating its robustness, adaptability, and the critical role of sequence modeling in LGPR-based localization.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1159-1169"},"PeriodicalIF":0.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918233","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}
In this article, a multiple-input and multiple-output (MIMO) arc synthetic aperture radar (ArcSAR) scheme for the detection of foreign object debris (FOD) located in an airport runway is proposed. In the proposed system, in order to overcome the problem of the large computational cost of the traditional wavenumber domain ArcSAR imaging method, an improved ArcSAR algorithm named segmentwise matched filtering (SWMF) is proposed to reduce the number of matched filters. Furthermore, to utilize the energy accumulation performance of multichannels in a monolithic microwave integrated circuit (MMIC), an MIMO-ArcSAR scheme is developed. In the MIMO-ArcSAR system, the multichannel phase compensation is performed to achieve coherent accumulation. To verify the performance of the proposed technology, an FOD detection radar system based on a low-cost MMIC chip is implemented. Simulation and real-data results show that the proposed method performs well in FOD detection.
{"title":"A Novel MIMO ArcSAR Imaging System for FOD Detection","authors":"Wenyan Hong;Tianyang Fan;Yixiong Zhang;Jianyang Zhou;Caipin Li;Steven Shichang Gao","doi":"10.1109/TRS.2025.3598677","DOIUrl":"https://doi.org/10.1109/TRS.2025.3598677","url":null,"abstract":"In this article, a multiple-input and multiple-output (MIMO) arc synthetic aperture radar (ArcSAR) scheme for the detection of foreign object debris (FOD) located in an airport runway is proposed. In the proposed system, in order to overcome the problem of the large computational cost of the traditional wavenumber domain ArcSAR imaging method, an improved ArcSAR algorithm named segmentwise matched filtering (SWMF) is proposed to reduce the number of matched filters. Furthermore, to utilize the energy accumulation performance of multichannels in a monolithic microwave integrated circuit (MMIC), an MIMO-ArcSAR scheme is developed. In the MIMO-ArcSAR system, the multichannel phase compensation is performed to achieve coherent accumulation. To verify the performance of the proposed technology, an FOD detection radar system based on a low-cost MMIC chip is implemented. Simulation and real-data results show that the proposed method performs well in FOD detection.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1145-1158"},"PeriodicalIF":0.0,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918205","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}
As radars are being increasingly used in autonomous driving, it is important to ensure that the results delivered by the radar sensors are trustworthy. Clustering and tracking of targets is part of the signal processing in such radar systems. Widely used system-on-chip (SoC) radars have vulnerabilities that affect trustworthiness. Therefore, a newly distributed architecture for clustering and tracking algorithms is introduced in this article, which can be implemented using multiple application-specific integrated circuits (ASICs). This architecture improves the protection of intellectual property (IP) related to the algorithms and improves the functional integrity. An adversary with access to a single ASIC of the proposed system cannot understand the system’s complete functionality with limited information stored in it. Further redundancy in tracking and a method to estimate the Jaccard Index of clusters are proposed to identify failures. Additionally, features—MIMO phase hopping based on phase coding of the Tx and randomly ordered clustering—are introduced to help identify potential data manipulation or failures and minimize the compromise on the delivered results. The effectiveness of the proposed distributed algorithm against the manipulation of certain information is demonstrated with simulated data.
{"title":"Distributed Clustering and Tracking Algorithm for Trustworthy MIMO Radars","authors":"Ram Kishore Arumugam;André Froehly;Patrick Wallrath;Reinhold Herschel;Nils Pohl","doi":"10.1109/TRS.2025.3597792","DOIUrl":"https://doi.org/10.1109/TRS.2025.3597792","url":null,"abstract":"As radars are being increasingly used in autonomous driving, it is important to ensure that the results delivered by the radar sensors are trustworthy. Clustering and tracking of targets is part of the signal processing in such radar systems. Widely used system-on-chip (SoC) radars have vulnerabilities that affect trustworthiness. Therefore, a newly distributed architecture for clustering and tracking algorithms is introduced in this article, which can be implemented using multiple application-specific integrated circuits (ASICs). This architecture improves the protection of intellectual property (IP) related to the algorithms and improves the functional integrity. An adversary with access to a single ASIC of the proposed system cannot understand the system’s complete functionality with limited information stored in it. Further redundancy in tracking and a method to estimate the Jaccard Index of clusters are proposed to identify failures. Additionally, features—MIMO phase hopping based on phase coding of the Tx and randomly ordered clustering—are introduced to help identify potential data manipulation or failures and minimize the compromise on the delivered results. The effectiveness of the proposed distributed algorithm against the manipulation of certain information is demonstrated with simulated data.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1119-1130"},"PeriodicalIF":0.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11122511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891081","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 : 2025-08-11DOI: 10.1109/TRS.2025.3597553
Lifan Xu;Shunqiao Sun;Ryan Wu;Binbin Shi;Jun Li
Sparse antenna arrays are widely used in automotive radar systems to achieve high angular resolution with reduced hardware complexity and mutual coupling. However, the high sidelobes inherent in sparse configurations can degrade angle estimation accuracy, motivating the need for effective interpolation methods. Although deep neural networks (DNNs) have shown strong performance in sparse signal recovery, their deployment is hindered by high computational cost, complex activation functions, and limited interpretability due to deep-layer architectures. To address these challenges, we propose a novel framework based on broad neural networks (BNNs) for efficient and accurate sparse signal retrieval. Unlike conventional DNNs, BNNs utilize parallel network structures with simplified, custom-designed activation functions, eliminating hidden layers to reduce computational overhead and improve transparency. We further enhance this design with an iterative BNN approach that incorporates a scalar mask and a phase-enhanced layer, enabling high-accuracy data recovery with fewer iterations. The experimental results on simulated and real-world radar datasets demonstrate that the proposed BNN framework significantly outperforms baseline methods in angle spectrum estimation and data interpolation while maintaining low computational complexity. These findings highlight the potential of BNNs as a practical, interpretable, and scalable alternative for advanced automotive radar applications.
{"title":"Broad Neural Networks for Sparse Signal Retrieval and Array Interpolation in Automotive Radar","authors":"Lifan Xu;Shunqiao Sun;Ryan Wu;Binbin Shi;Jun Li","doi":"10.1109/TRS.2025.3597553","DOIUrl":"https://doi.org/10.1109/TRS.2025.3597553","url":null,"abstract":"Sparse antenna arrays are widely used in automotive radar systems to achieve high angular resolution with reduced hardware complexity and mutual coupling. However, the high sidelobes inherent in sparse configurations can degrade angle estimation accuracy, motivating the need for effective interpolation methods. Although deep neural networks (DNNs) have shown strong performance in sparse signal recovery, their deployment is hindered by high computational cost, complex activation functions, and limited interpretability due to deep-layer architectures. To address these challenges, we propose a novel framework based on broad neural networks (BNNs) for efficient and accurate sparse signal retrieval. Unlike conventional DNNs, BNNs utilize parallel network structures with simplified, custom-designed activation functions, eliminating hidden layers to reduce computational overhead and improve transparency. We further enhance this design with an iterative BNN approach that incorporates a scalar mask and a phase-enhanced layer, enabling high-accuracy data recovery with fewer iterations. The experimental results on simulated and real-world radar datasets demonstrate that the proposed BNN framework significantly outperforms baseline methods in angle spectrum estimation and data interpolation while maintaining low computational complexity. These findings highlight the potential of BNNs as a practical, interpretable, and scalable alternative for advanced automotive radar applications.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1103-1118"},"PeriodicalIF":0.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891080","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 : 2025-08-08DOI: 10.1109/TRS.2025.3597095
Aitor Correas-Serrano;Nikita Petrov;Maria A. Gonzalez-Huici;Alexander Yarovoy
The effect of amplifier-related signal amplitude compression in orthogonal time–frequency space (OTFS) waveform for radar and communications systems is considered. A novel approach to OTFS waveform generation is proposed, where complementary sequences are used with the Zak transform to encode delay-Doppler symbols and form an OTFS time-domain signal with a constant envelope. The high peak-to-average power ratio (PAPR) of conventional OTFS can cause amplifier saturation, leading to spectral noise and performance degradation in both communication and radar systems due to amplitude clipping. This issue can be critical in dual-function radar and communication applications, where high power may be crucial in both use cases. The proposed waveform, namely, constant modulus OTFS (CM-OTFS), offers an alternative to standard OTFS when high-power or low-cost amplification is required. The sensing and communications performances of CM-OTFS are evaluated through numerical simulations and compared with pristine and amplifier-distorted OTFS waveforms. CM-OTFS demonstrates slightly degraded sensing performance and lower communication rate than pristine OTFS but outperforms amplifier-distorted OTFS signals. The performance of CM-OTFS is evaluated through radar and communication simulations, as well as radar measurements using the waveform-agile PARSAX radar.
{"title":"Constant Modulus OTFS Based on Zak Transform of Complementary Sequences for Joint Radar and Communications","authors":"Aitor Correas-Serrano;Nikita Petrov;Maria A. Gonzalez-Huici;Alexander Yarovoy","doi":"10.1109/TRS.2025.3597095","DOIUrl":"https://doi.org/10.1109/TRS.2025.3597095","url":null,"abstract":"The effect of amplifier-related signal amplitude compression in orthogonal time–frequency space (OTFS) waveform for radar and communications systems is considered. A novel approach to OTFS waveform generation is proposed, where complementary sequences are used with the Zak transform to encode delay-Doppler symbols and form an OTFS time-domain signal with a constant envelope. The high peak-to-average power ratio (PAPR) of conventional OTFS can cause amplifier saturation, leading to spectral noise and performance degradation in both communication and radar systems due to amplitude clipping. This issue can be critical in dual-function radar and communication applications, where high power may be crucial in both use cases. The proposed waveform, namely, constant modulus OTFS (CM-OTFS), offers an alternative to standard OTFS when high-power or low-cost amplification is required. The sensing and communications performances of CM-OTFS are evaluated through numerical simulations and compared with pristine and amplifier-distorted OTFS waveforms. CM-OTFS demonstrates slightly degraded sensing performance and lower communication rate than pristine OTFS but outperforms amplifier-distorted OTFS signals. The performance of CM-OTFS is evaluated through radar and communication simulations, as well as radar measurements using the waveform-agile PARSAX radar.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1131-1144"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11121347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896770","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 : 2025-08-05DOI: 10.1109/TRS.2025.3595922
Yingwei Tian;Xinyi Ma;Jing Yang;Jiurui Zhao
The compact high-frequency surface wave radar (HFSWR) has the potential to provide over-the-horizon maritime surveillance at a low cost and with flexible installation. Nonetheless, due to its low transmit power and antenna gain, the compact HFSWR tends to have high false and missing alarm rates for target detection and a large error in the target’s azimuth measurement, which leads to poor target association performance and is consequently detrimental to track acquisition. In this article, a multitarget data association method based on cascaded neural networks is proposed. This cascaded structure consists of two stages: interframe filtering and interframe association. In the first stage, a 3D-UNet model is used to filter out false alarms based on different interframe variation characteristics of the targets and false alarms. In the second stage, a graph neural network (GNN) model is used to realize target association and obtain multiple trajectories on the range-Doppler (RD)-frame spectrum. Finally, the output trajectory segments belonging to the same target are further associated using Kalman filtering to suppress the track breakage problem caused by missed detections. Both simulation and experimental results show that the proposed method can effectively realize multitarget association and is especially advantageous in the case of high false and missing alarm rates and short target trajectories.
{"title":"Multitarget Data Association Based on Cascaded Neural Networks for Compact HFSWR","authors":"Yingwei Tian;Xinyi Ma;Jing Yang;Jiurui Zhao","doi":"10.1109/TRS.2025.3595922","DOIUrl":"https://doi.org/10.1109/TRS.2025.3595922","url":null,"abstract":"The compact high-frequency surface wave radar (HFSWR) has the potential to provide over-the-horizon maritime surveillance at a low cost and with flexible installation. Nonetheless, due to its low transmit power and antenna gain, the compact HFSWR tends to have high false and missing alarm rates for target detection and a large error in the target’s azimuth measurement, which leads to poor target association performance and is consequently detrimental to track acquisition. In this article, a multitarget data association method based on cascaded neural networks is proposed. This cascaded structure consists of two stages: interframe filtering and interframe association. In the first stage, a 3D-UNet model is used to filter out false alarms based on different interframe variation characteristics of the targets and false alarms. In the second stage, a graph neural network (GNN) model is used to realize target association and obtain multiple trajectories on the range-Doppler (RD)-frame spectrum. Finally, the output trajectory segments belonging to the same target are further associated using Kalman filtering to suppress the track breakage problem caused by missed detections. Both simulation and experimental results show that the proposed method can effectively realize multitarget association and is especially advantageous in the case of high false and missing alarm rates and short target trajectories.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1073-1085"},"PeriodicalIF":0.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843088","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 : 2025-07-30DOI: 10.1109/TRS.2025.3594236
Alexandre Bordat;Claire Béranger;Michel Chapron;Petr Dobias;Julien Le Kernec;David Guyard;Olivier Romain
Falls among the elderly present a significant healthcare and socioeconomic challenge. The timed up and go (TUG) test is a widely used tool for assessing mobility and fall risk. However, traditional methods are limited in terms of objectivity and scalability. This study introduces a radar-based approach to automating the TUG test using frequency-modulated continuous-wave (FMCW) radar. The main objective is to provide an automated, nonintrusive, and privacy-preserving system for fall risk assessment through precise segmentation of TUG test phases (standing up, walking, turning around, and sitting down) and extraction of key gait parameters, such as walking speed, distance traveled, and phase durations. Validated against a cohort of 100 participants, the system achieved a mean relative velocity error of 8.89% and a mean absolute time error of 0.159 s. These results demonstrate high accuracy and robustness, making it a promising tool for fall risk assessment in both clinical and home environments. The strong correlation agreement for studied metrics (asymmetry and cadence) is confirmed by the intraclass correlation coefficient (ICC) between Motion Capture (MoCap) and radar, with $text {ICC}_{text {asymmetry}} = 81.7%$ and $text {ICC}_{text {cadence}} = 76.2%$ . Additionally, the Bland–Altman analysis further supports this agreement, showing a strong concordance between the radar and MoCap measurements for both metrics.
{"title":"Toward Automated Fall Risk Assessment: Validation of an FMCW Radar-Based Timed Up and Go Test","authors":"Alexandre Bordat;Claire Béranger;Michel Chapron;Petr Dobias;Julien Le Kernec;David Guyard;Olivier Romain","doi":"10.1109/TRS.2025.3594236","DOIUrl":"https://doi.org/10.1109/TRS.2025.3594236","url":null,"abstract":"Falls among the elderly present a significant healthcare and socioeconomic challenge. The timed up and go (TUG) test is a widely used tool for assessing mobility and fall risk. However, traditional methods are limited in terms of objectivity and scalability. This study introduces a radar-based approach to automating the TUG test using frequency-modulated continuous-wave (FMCW) radar. The main objective is to provide an automated, nonintrusive, and privacy-preserving system for fall risk assessment through precise segmentation of TUG test phases (standing up, walking, turning around, and sitting down) and extraction of key gait parameters, such as walking speed, distance traveled, and phase durations. Validated against a cohort of 100 participants, the system achieved a mean relative velocity error of 8.89% and a mean absolute time error of 0.159 s. These results demonstrate high accuracy and robustness, making it a promising tool for fall risk assessment in both clinical and home environments. The strong correlation agreement for studied metrics (asymmetry and cadence) is confirmed by the intraclass correlation coefficient (ICC) between Motion Capture (MoCap) and radar, with <inline-formula> <tex-math>$text {ICC}_{text {asymmetry}} = 81.7%$ </tex-math></inline-formula> and <inline-formula> <tex-math>$text {ICC}_{text {cadence}} = 76.2%$ </tex-math></inline-formula>. Additionally, the Bland–Altman analysis further supports this agreement, showing a strong concordance between the radar and MoCap measurements for both metrics.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1086-1102"},"PeriodicalIF":0.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868272","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}
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