Pub Date : 2025-11-11DOI: 10.1109/TRS.2025.3631021
Tao Zhang;Nishang Xie;Sinong Quan;Wei Wang;Feiming Wei;Wenxian Yu
In the past few years, polarimetric synthetic aperture radar (PolSAR) as an advanced technology has been widely exploited to Earth observation, among which ship detection is an active research topic. Taking the sub-look decomposition technology as the basis, this article proposes a new ship detection method, abbreviated to amplitude-based ship detection metric (ASM). In brief, two single-look complex (SLC) images <inline-formula> <tex-math>$I_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math>$I_{2}$ </tex-math></inline-formula> are first obtained from the original PolSAR image <inline-formula> <tex-math>$O$ </tex-math></inline-formula> for forming the data group {<inline-formula> <tex-math>$I_{1}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$O$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$I_{2}$ </tex-math></inline-formula>}. Then, the <inline-formula> <tex-math>$H/A/alpha $ </tex-math></inline-formula> decomposition is performed on {<inline-formula> <tex-math>$I_{1}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$O$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$I_{2}$ </tex-math></inline-formula>} so as to yield the <inline-formula> <tex-math>$H/alpha $ </tex-math></inline-formula> plane group {<inline-formula> <tex-math>$P_{1}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$P_{0}$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$P_{2}$ </tex-math></inline-formula>}, which is subsequently used to suppress sea clutter and generate another filtered data group {<inline-formula> <tex-math>$F_{1}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$F_{0}$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$F_{2}$ </tex-math></inline-formula>} that, respectively, corresponds to <inline-formula> <tex-math>$I_{1}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$O$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$I_{2}$ </tex-math></inline-formula>. Thereafter, a new <inline-formula> <tex-math>$3 times 3$ </tex-math></inline-formula> spatial–spectral coherence difference matrix [<inline-formula> <tex-math>$ST$ </tex-math></inline-formula>] is further constructed by {<inline-formula> <tex-math>$F_{1}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$F_{0}$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$F_{2}$ </tex-math></inline-formula>}, wherein the spatial information and spectral information are simultaneously used. Therefore, [<inline-formula> <tex-math>$ST$ </tex-math></inline-formula>] can effectively highlight ships from sea clutter. To verify this point, an ASM is finally built by multiplying the amplitude values of the terms <inline-formula> <tex-math>$text {ST}_{13}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$text {ST}_{23}$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$text {ST}_{33}$ </tex-math></inline-formula> together. Extensive experiments demonstrate
{"title":"Polarimeric SAR Ship Detection Based on Sub-Look the Decomposition Technology","authors":"Tao Zhang;Nishang Xie;Sinong Quan;Wei Wang;Feiming Wei;Wenxian Yu","doi":"10.1109/TRS.2025.3631021","DOIUrl":"https://doi.org/10.1109/TRS.2025.3631021","url":null,"abstract":"In the past few years, polarimetric synthetic aperture radar (PolSAR) as an advanced technology has been widely exploited to Earth observation, among which ship detection is an active research topic. Taking the sub-look decomposition technology as the basis, this article proposes a new ship detection method, abbreviated to amplitude-based ship detection metric (ASM). In brief, two single-look complex (SLC) images <inline-formula> <tex-math>$I_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math>$I_{2}$ </tex-math></inline-formula> are first obtained from the original PolSAR image <inline-formula> <tex-math>$O$ </tex-math></inline-formula> for forming the data group {<inline-formula> <tex-math>$I_{1}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$O$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$I_{2}$ </tex-math></inline-formula>}. Then, the <inline-formula> <tex-math>$H/A/alpha $ </tex-math></inline-formula> decomposition is performed on {<inline-formula> <tex-math>$I_{1}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$O$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$I_{2}$ </tex-math></inline-formula>} so as to yield the <inline-formula> <tex-math>$H/alpha $ </tex-math></inline-formula> plane group {<inline-formula> <tex-math>$P_{1}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$P_{0}$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$P_{2}$ </tex-math></inline-formula>}, which is subsequently used to suppress sea clutter and generate another filtered data group {<inline-formula> <tex-math>$F_{1}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$F_{0}$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$F_{2}$ </tex-math></inline-formula>} that, respectively, corresponds to <inline-formula> <tex-math>$I_{1}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$O$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$I_{2}$ </tex-math></inline-formula>. Thereafter, a new <inline-formula> <tex-math>$3 times 3$ </tex-math></inline-formula> spatial–spectral coherence difference matrix [<inline-formula> <tex-math>$ST$ </tex-math></inline-formula>] is further constructed by {<inline-formula> <tex-math>$F_{1}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$F_{0}$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$F_{2}$ </tex-math></inline-formula>}, wherein the spatial information and spectral information are simultaneously used. Therefore, [<inline-formula> <tex-math>$ST$ </tex-math></inline-formula>] can effectively highlight ships from sea clutter. To verify this point, an ASM is finally built by multiplying the amplitude values of the terms <inline-formula> <tex-math>$text {ST}_{13}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$text {ST}_{23}$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$text {ST}_{33}$ </tex-math></inline-formula> together. Extensive experiments demonstrate ","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"35-49"},"PeriodicalIF":0.0,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729504","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 : 2025-11-03DOI: 10.1109/TRS.2025.3628294
Kuiyu Chen;Chen Liu;Yunchao Song;Lingzhi Zhu
Human activity recognition (HAR) has emerged as a key technology, with applications ranging from security to healthcare. Radar-based HAR, which leverages micro-Doppler signatures, offers strong performance in complex environments. However, most existing systems operate under closed-set assumptions, recognizing only predefined activities. This restricts their effectiveness in real-world scenarios where novel or unseen activities may occur. To address this challenge, this work proposes a virtual prototype learning (VPL) framework for open-set HAR. Inspired by human memory and pattern-matching processes, VPL encodes micro-Doppler spectrograms into abstract representations and compares them with learned prototypes in the metric space. The framework is guided by a combination of Euclidean cross-entropy loss and clustering loss to promote clear separation between different activity classes while preserving consistency within each class. To further improve robustness, VPL incorporates a manifold mixup strategy, generating pseudo-samples that help sharpen the boundary between known and unknown activities. A buffer zone is established in the feature space to reinforce this separation, and hyperspherical decision boundaries are employed to enhance classification accuracy. Experiments with real-world radar data show that VPL outperforms existing methods, achieving higher accuracy for known activities while effectively detecting unknown activities.
{"title":"Open-Set Human Activity Recognition With Micro-Doppler Signatures and Virtual Prototype Learning","authors":"Kuiyu Chen;Chen Liu;Yunchao Song;Lingzhi Zhu","doi":"10.1109/TRS.2025.3628294","DOIUrl":"https://doi.org/10.1109/TRS.2025.3628294","url":null,"abstract":"Human activity recognition (HAR) has emerged as a key technology, with applications ranging from security to healthcare. Radar-based HAR, which leverages micro-Doppler signatures, offers strong performance in complex environments. However, most existing systems operate under closed-set assumptions, recognizing only predefined activities. This restricts their effectiveness in real-world scenarios where novel or unseen activities may occur. To address this challenge, this work proposes a virtual prototype learning (VPL) framework for open-set HAR. Inspired by human memory and pattern-matching processes, VPL encodes micro-Doppler spectrograms into abstract representations and compares them with learned prototypes in the metric space. The framework is guided by a combination of Euclidean cross-entropy loss and clustering loss to promote clear separation between different activity classes while preserving consistency within each class. To further improve robustness, VPL incorporates a manifold mixup strategy, generating pseudo-samples that help sharpen the boundary between known and unknown activities. A buffer zone is established in the feature space to reinforce this separation, and hyperspherical decision boundaries are employed to enhance classification accuracy. Experiments with real-world radar data show that VPL outperforms existing methods, achieving higher accuracy for known activities while effectively detecting unknown activities.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1463-1473"},"PeriodicalIF":0.0,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560632","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}
Polarimetry, which is the ability to measure the scattering response of the environment across orthogonal polarizations, is fundamental to enhancing wireless communication and radar system performance. In this article, we use the Zak-OTFS modulation to enable instantaneous polarimetry within a single transmission frame. We transmit a Zak-OTFS carrier waveform and a spread carrier waveform mutually unbiased to it simultaneously over orthogonal polarizations. The mutual unbiasedness of the two waveforms enables the receiver to estimate the full polarimetric response of the scattering environment from a single received frame. Unlike existing methods for instantaneous polarimetry with computational complexity quadratic in the time–bandwidth product, the proposed method enables instantaneous polarimetry at near-linear complexity in the time–bandwidth product. Via numerical simulations, we show ideal polarimetric target detection and parameter estimation results with the proposed method, with improvements in computational complexity and greater clutter resilience over comparable baselines.
{"title":"Instantaneous Polarimetry With Zak-OTFS","authors":"Nishant Mehrotra;Sandesh Rao Mattu;Robert Calderbank","doi":"10.1109/TRS.2025.3625812","DOIUrl":"https://doi.org/10.1109/TRS.2025.3625812","url":null,"abstract":"Polarimetry, which is the ability to measure the scattering response of the environment across orthogonal polarizations, is fundamental to enhancing wireless communication and radar system performance. In this article, we use the Zak-OTFS modulation to enable instantaneous polarimetry within a single transmission frame. We transmit a Zak-OTFS carrier waveform and a spread carrier waveform mutually unbiased to it simultaneously over orthogonal polarizations. The mutual unbiasedness of the two waveforms enables the receiver to estimate the full polarimetric response of the scattering environment from a single received frame. Unlike existing methods for instantaneous polarimetry with computational complexity quadratic in the time–bandwidth product, the proposed method enables instantaneous polarimetry at near-linear complexity in the time–bandwidth product. Via numerical simulations, we show ideal polarimetric target detection and parameter estimation results with the proposed method, with improvements in computational complexity and greater clutter resilience over comparable baselines.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1413-1420"},"PeriodicalIF":0.0,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510157","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 : 2025-10-20DOI: 10.1109/TRS.2025.3623966
Zirui Chen;Yifei Ji;Yongsheng Zhang;Zhen Dong;Weijian Liu;Junqiang Song
The nonstationary spatiotemporal distribution of the ionosphere creates multiple irregular propagation paths between the target and transceivers of the skywave over-the-horizon radar (OTHR). The multipath effect fundamentally induces distortions of the target plot signatures in range and Doppler dimensions and thereby significantly degrades the target localization/velocimetry accuracy and detection performance. Building upon the full-link sea clutter model established in Part I, this article develops a comprehensive framework incorporating trans-ionospheric signal modeling, simulation, and impact analysis for multipath targets. First, a variable-step ray-tracing technique generally following the coarse-to-fine search mechanism is developed to identify all propagation paths illuminating targets within a wide radar beam. Second, full-link multipath signal models in the fast-slow-time and range–Doppler (RD) domains are established by integrating ionospheric effects with high-frequency (HF) radar cross section (RCS) of typical targets. Finally, a theoretical analysis of multipath effects on target plot is performed based on the RD model. Three types of typical modes, large-scale multipath, microscale multipath, and multihop multipath, are defined by propagation path characteristics. Their impacts are analyzed for aerial and maritime OTHR detection scenarios. Theoretical and simulation results quantitatively characterize the impact of multipath effects on target signatures, demonstrating that trans-ionospheric multipath effects provide critical information for parameter estimation enhancement. The proposed OTHR full-link model establishes a theoretical framework for understanding trans-ionospheric multipath effects and provides foundational support for enhancing localization/velocimetry accuracy, suppressing false target plots, resolving Doppler ambiguity, and improving detection performance.
{"title":"Skywave OTHR Full-Link Modeling and Simulation—Part II: Trans-Ionospheric Multipath Target Signal","authors":"Zirui Chen;Yifei Ji;Yongsheng Zhang;Zhen Dong;Weijian Liu;Junqiang Song","doi":"10.1109/TRS.2025.3623966","DOIUrl":"https://doi.org/10.1109/TRS.2025.3623966","url":null,"abstract":"The nonstationary spatiotemporal distribution of the ionosphere creates multiple irregular propagation paths between the target and transceivers of the skywave over-the-horizon radar (OTHR). The multipath effect fundamentally induces distortions of the target plot signatures in range and Doppler dimensions and thereby significantly degrades the target localization/velocimetry accuracy and detection performance. Building upon the full-link sea clutter model established in Part I, this article develops a comprehensive framework incorporating trans-ionospheric signal modeling, simulation, and impact analysis for multipath targets. First, a variable-step ray-tracing technique generally following the coarse-to-fine search mechanism is developed to identify all propagation paths illuminating targets within a wide radar beam. Second, full-link multipath signal models in the fast-slow-time and range–Doppler (RD) domains are established by integrating ionospheric effects with high-frequency (HF) radar cross section (RCS) of typical targets. Finally, a theoretical analysis of multipath effects on target plot is performed based on the RD model. Three types of typical modes, large-scale multipath, microscale multipath, and multihop multipath, are defined by propagation path characteristics. Their impacts are analyzed for aerial and maritime OTHR detection scenarios. Theoretical and simulation results quantitatively characterize the impact of multipath effects on target signatures, demonstrating that trans-ionospheric multipath effects provide critical information for parameter estimation enhancement. The proposed OTHR full-link model establishes a theoretical framework for understanding trans-ionospheric multipath effects and provides foundational support for enhancing localization/velocimetry accuracy, suppressing false target plots, resolving Doppler ambiguity, and improving detection performance.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1392-1412"},"PeriodicalIF":0.0,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455818","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}
Radar-based object detection (OD) is critical for detecting distant objects and ensuring privacy in challenging environments. Existing OD pipelines require extensive preprocessing and complex machine learning (ML) algorithms, which hinders edge deployment. Prior approaches address these challenges by processing raw radar data using an analog-to-digital converter (ADC) or fast Fourier transform (FFT)-based preprocessing. However, as sensing resolution increases, the volume of data generated at sensor nodes grows, leading to increased model complexity and computational overhead on edge systems. In this work, we introduce ChirpNet, a neural network designed for radar-based OD. ChirpNet processes raw ADC data from virtual antennas for each chirp, integrating sequential chirp-based radar sensing directly into the network. This design achieves a $43times $ reduction in model computations and a $5times $ reduction in latency while still maintaining competitive object detection performance. Additionally, the ChirpNet models demonstrate improved robustness in various clutter scenarios compared to prior ML-based detectors.
{"title":"Toward Efficient and Robust Sequential Chirp-Based Data-Driven Radar Processing for Object Detection","authors":"Sudarshan Sharma;Hemant Kumawat;Anuvab Sen;Jinhyeok Park;Saibal Mukhopadhyay","doi":"10.1109/TRS.2025.3622514","DOIUrl":"https://doi.org/10.1109/TRS.2025.3622514","url":null,"abstract":"Radar-based object detection (OD) is critical for detecting distant objects and ensuring privacy in challenging environments. Existing OD pipelines require extensive preprocessing and complex machine learning (ML) algorithms, which hinders edge deployment. Prior approaches address these challenges by processing raw radar data using an analog-to-digital converter (ADC) or fast Fourier transform (FFT)-based preprocessing. However, as sensing resolution increases, the volume of data generated at sensor nodes grows, leading to increased model complexity and computational overhead on edge systems. In this work, we introduce ChirpNet, a neural network designed for radar-based OD. ChirpNet processes raw ADC data from virtual antennas for each chirp, integrating sequential chirp-based radar sensing directly into the network. This design achieves a <inline-formula> <tex-math>$43times $ </tex-math></inline-formula> reduction in model computations and a <inline-formula> <tex-math>$5times $ </tex-math></inline-formula> reduction in latency while still maintaining competitive object detection performance. Additionally, the ChirpNet models demonstrate improved robustness in various clutter scenarios compared to prior ML-based detectors.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1435-1448"},"PeriodicalIF":0.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560633","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 : 2025-10-16DOI: 10.1109/TRS.2025.3622484
Yihan Su;Lei Wang;Xinan Lu;Cenwei Liu;Yimin Liu
Modern radars face the threat of multiple mainlobe jammings, and the use of distributed radars for jamming suppression has received extensive attention. Most existing studies primarily focus on the narrowband or far-field jamming scenarios, where jamming signals are assumed to be time-aligned across radars. However, for wideband or large-scale distributed radar systems, the time-delay differences of jamming signals across different radar nodes become nonnegligible, leading to the failure of classical algorithms. Considering the jamming delay differences, this article proposes a multijamming suppression method based on reconstruction of the jamming signals, where an alternative iteration is adopted to integrate the jamming signal reconstruction and time-delay difference estimation. Appropriate initialization and waveform design enable the proposed algorithm to be effectively applied across different jamming types, including noise jamming and interrupted sampling repeater jamming (ISRJ). Both the simulation and measured data experiments validate the effectiveness of the proposed algorithm to suppress multiple jammings.
{"title":"Multiple Mainlobe Jamming Reconstruction and Suppression in Wideband Distributed Radars","authors":"Yihan Su;Lei Wang;Xinan Lu;Cenwei Liu;Yimin Liu","doi":"10.1109/TRS.2025.3622484","DOIUrl":"https://doi.org/10.1109/TRS.2025.3622484","url":null,"abstract":"Modern radars face the threat of multiple mainlobe jammings, and the use of distributed radars for jamming suppression has received extensive attention. Most existing studies primarily focus on the narrowband or far-field jamming scenarios, where jamming signals are assumed to be time-aligned across radars. However, for wideband or large-scale distributed radar systems, the time-delay differences of jamming signals across different radar nodes become nonnegligible, leading to the failure of classical algorithms. Considering the jamming delay differences, this article proposes a multijamming suppression method based on reconstruction of the jamming signals, where an alternative iteration is adopted to integrate the jamming signal reconstruction and time-delay difference estimation. Appropriate initialization and waveform design enable the proposed algorithm to be effectively applied across different jamming types, including noise jamming and interrupted sampling repeater jamming (ISRJ). Both the simulation and measured data experiments validate the effectiveness of the proposed algorithm to suppress multiple jammings.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1362-1374"},"PeriodicalIF":0.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405286","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 : 2025-10-10DOI: 10.1109/TRS.2025.3620087
Mohammed Aasim Shaikh;Geethu Joseph;Ashish Pandharipande;Nitin Jonathan Myers
Digital radars with low-resolution analog-to-digital converters (ADCs) have attracted attention as a solution to reducing the high digital processing complexity and power consumption at the receiver. Radars employing low-resolution ADCs, however, have a limited dynamic range, due to which high-radar cross section (RCS) targets mask low-RCS targets. The masking occurs because the quantized output is primarily determined by returns from high-RCS targets. To enhance the dynamic range of such radars, we propose to operate the ADC at a high resolution in the initial slow-time slot of each radar frame. The resulting high-resolution measurements are used together with the known Doppler statistics of dominant targets to construct a dither signal, which is used as a quantization threshold to acquire low-resolution ADC measurements in the subsequent slow-time slots. By incorporating situation awareness in the form of Doppler statistics, our dither signal can suppress returns from strong targets, effectively unmasking weak targets with low-resolution measurements. We analyze system performance in terms of the probability of detection and show that the proposed approach outperforms existing methods in enhancing the detection of weak targets. The simulations demonstrate that our method significantly improves target detection and reduces the normalized mean square error (NMSE) in the estimated radar channel over comparable benchmarks.
{"title":"Situation-Aware Dithering for Dynamic Range Enhancement of a Mixed-Resolution ADC in Automotive Radar Receivers","authors":"Mohammed Aasim Shaikh;Geethu Joseph;Ashish Pandharipande;Nitin Jonathan Myers","doi":"10.1109/TRS.2025.3620087","DOIUrl":"https://doi.org/10.1109/TRS.2025.3620087","url":null,"abstract":"Digital radars with low-resolution analog-to-digital converters (ADCs) have attracted attention as a solution to reducing the high digital processing complexity and power consumption at the receiver. Radars employing low-resolution ADCs, however, have a limited dynamic range, due to which high-radar cross section (RCS) targets mask low-RCS targets. The masking occurs because the quantized output is primarily determined by returns from high-RCS targets. To enhance the dynamic range of such radars, we propose to operate the ADC at a high resolution in the initial slow-time slot of each radar frame. The resulting high-resolution measurements are used together with the known Doppler statistics of dominant targets to construct a dither signal, which is used as a quantization threshold to acquire low-resolution ADC measurements in the subsequent slow-time slots. By incorporating situation awareness in the form of Doppler statistics, our dither signal can suppress returns from strong targets, effectively unmasking weak targets with low-resolution measurements. We analyze system performance in terms of the probability of detection and show that the proposed approach outperforms existing methods in enhancing the detection of weak targets. The simulations demonstrate that our method significantly improves target detection and reduces the normalized mean square error (NMSE) in the estimated radar channel over comparable benchmarks.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1474-1488"},"PeriodicalIF":0.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612105","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 : 2025-10-07DOI: 10.1109/TRS.2025.3618755
Ahmed A. Abouelfadl;Ioannis Psaromiligkos;Benoit Champagne
A characteristic feature of cognitive radars is the ability to adapt their transmitted waveforms to the impulse response of the target of interest. A typical assumption is to represent the evolution of the target impulse response (TIR) using the Gaussian linear state space (LSS) model. Based on this assumption, the Kalman filter (KF) has been used to estimate the TIR as the optimal Bayesian filter under known target and interference statistics. In practice, however, the available measured data for different targets suggest non-Gaussian TIR distributions and do not justify the assumption of an LSS generating model. In this article, we propose a new TIR tracking method based on Bayesian nonparametric (BNP) statistics. In contrast to conventional Bayesian filters, such as KF or particle filter (PF), the proposed method does not require prior knowledge about the target or environmental interference statistics. This added flexibility allows us to consider non-Gaussian TIR distributions, which have not been examined in the literature heretofore. Furthermore, we propose a new TIR generating model based on the spherical invariant random process, which stands as a more realistic approach supported by published empirical data. Through extensive Monte Carlo simulations, we show that the proposed BNP method offers improved TIR tracking accuracy compared with the conventional Bayesian filters under several distributions and generating models, even in harsh environments like jamming. Notably, this superior performance comes with lower complexity and without prior knowledge about the target statistics as required by the conventional Bayesian filters.
{"title":"Bayesian Nonparametric Tracking of Target Impulse Response for Cognitive Radars","authors":"Ahmed A. Abouelfadl;Ioannis Psaromiligkos;Benoit Champagne","doi":"10.1109/TRS.2025.3618755","DOIUrl":"https://doi.org/10.1109/TRS.2025.3618755","url":null,"abstract":"A characteristic feature of cognitive radars is the ability to adapt their transmitted waveforms to the impulse response of the target of interest. A typical assumption is to represent the evolution of the target impulse response (TIR) using the Gaussian linear state space (LSS) model. Based on this assumption, the Kalman filter (KF) has been used to estimate the TIR as the optimal Bayesian filter under known target and interference statistics. In practice, however, the available measured data for different targets suggest non-Gaussian TIR distributions and do not justify the assumption of an LSS generating model. In this article, we propose a new TIR tracking method based on Bayesian nonparametric (BNP) statistics. In contrast to conventional Bayesian filters, such as KF or particle filter (PF), the proposed method does not require prior knowledge about the target or environmental interference statistics. This added flexibility allows us to consider non-Gaussian TIR distributions, which have not been examined in the literature heretofore. Furthermore, we propose a new TIR generating model based on the spherical invariant random process, which stands as a more realistic approach supported by published empirical data. Through extensive Monte Carlo simulations, we show that the proposed BNP method offers improved TIR tracking accuracy compared with the conventional Bayesian filters under several distributions and generating models, even in harsh environments like jamming. Notably, this superior performance comes with lower complexity and without prior knowledge about the target statistics as required by the conventional Bayesian filters.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1375-1391"},"PeriodicalIF":0.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455821","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 : 2025-10-06DOI: 10.1109/TRS.2025.3618481
Zheng Cao;Jisheng Dai;Weichao Xu;Xue-Qin Jiang
This article addresses the problem of range-Doppler estimation for multiple moving far-field targets using a wideband frequency-modulated continuous wave (FMCW) radar. Current state-of-the-art techniques typically employ a multistep approach to estimate range and Doppler separately, which can lead to substantial performance degradation. Some sparse representation (SR) methods utilize a vectorization operation to recover the sparse solution for the joint range-Doppler estimation, but this will bring a heavy computational burden. To overcome these shortcomings, in this article, we propose an efficient sparsity learning method for joint range-Doppler estimation. We first formulate the FMCW beat signal model as a multidimensional SR form and systematically tackle the model mismatch issue within the Bayesian framework. Subsequently, we incorporate an auxiliary variable for Bayesian formulation to help characterize the range and Doppler individually. Finally, we devise a decoupled message passing approach to provide a superior resolution on joint range and Doppler estimation beyond the Rayleigh resolution limit while yielding a significant computational complexity reduction. Simulation results demonstrate the effectiveness of the proposed method.
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Pub Date : 2025-10-06DOI: 10.1109/TRS.2025.3618085
Jeroen Overdevest;Jiaqi Ji;Arie G. C. Koppelaar;Ashish Pandharipande;Ruud J. G. van Sloun
phase-modulated continuous wave (PMCW) radar has gained significant interest due to highly flexible waveform designs and multiple-input–multiple-output (MIMO) scaling to achieve higher angular resolutions. However, imperfect code orthogonality and the presence of self-interference (SI) limit its applicability today due to insufficient dynamic range, when compared to frequency-modulated continuous wave (FMCW) radar. This work introduces a deep-unfolded successive network that aims at increasing the dynamic range in terms of detectable targets, i.e, detecting weaker targets in the presence of strong targets, after range-Doppler (RD) processing in code-division multiplexed PMCW radar. The successive network uses sparse recovery with group $ell _{1}$ -regularization for sidelobe suppression. Through an ablation study, we substantiate that the proposed successive unrolled network outperforms the conventional unrolled network in terms of both magnitude and phase estimation accuracy. Moreover, we present how the proposed successive network robustly scales to large MIMO configurations (up to 32 transmit antennas), where the conventional methods tend to fail. Successive learned FISTA (L-FISTA) achieves a dynamic range of 99.5 dB for a $32times 4$ PMCW radar. Additionally, the methods are evaluated at various levels of sparsity, using range-Doppler maps (RD maps) in dense target scenarios. Finally, we compare the computational load of the presented methods using the floating-point operations (FLOPs) metric.
相位调制连续波（PMCW）雷达由于其高度灵活的波形设计和多输入多输出（MIMO）缩放以实现更高的角度分辨率而获得了极大的兴趣。然而，与调频连续波（FMCW）雷达相比，由于动态范围不足，编码正交性不完善和自干扰（SI）的存在限制了它在当今的适用性。本文介绍了一种深度展开的连续网络，其目的是在可探测目标方面增加动态范围，即在强目标存在下检测弱目标，在码分复用PMCW雷达中进行距离多普勒（RD）处理。连续网络采用组$ well _{1}$ -正则化稀疏恢复来抑制副瓣。通过消融研究，我们证实了所提出的连续展开网络在幅度和相位估计精度方面都优于传统展开网络。此外，我们还介绍了所提出的连续网络如何稳健地扩展到大型MIMO配置（多达32个发射天线），而传统方法往往会失败。连续学习FISTA （L-FISTA）在32 × 4$ PMCW雷达上实现了99.5 dB的动态范围。此外，利用距离-多普勒图（RD图）在密集目标场景中对不同稀疏度的方法进行了评估。最后，我们使用浮点运算（FLOPs）度量比较了所提出方法的计算负载。
{"title":"High-Dynamic Range PMCW Radar Sensing Through Deep-Unfolded Successive Sparse Recovery","authors":"Jeroen Overdevest;Jiaqi Ji;Arie G. C. Koppelaar;Ashish Pandharipande;Ruud J. G. van Sloun","doi":"10.1109/TRS.2025.3618085","DOIUrl":"https://doi.org/10.1109/TRS.2025.3618085","url":null,"abstract":"phase-modulated continuous wave (PMCW) radar has gained significant interest due to highly flexible waveform designs and multiple-input–multiple-output (MIMO) scaling to achieve higher angular resolutions. However, imperfect code orthogonality and the presence of self-interference (SI) limit its applicability today due to insufficient dynamic range, when compared to frequency-modulated continuous wave (FMCW) radar. This work introduces a deep-unfolded successive network that aims at increasing the dynamic range in terms of detectable targets, i.e, detecting weaker targets in the presence of strong targets, after range-Doppler (RD) processing in code-division multiplexed PMCW radar. The successive network uses sparse recovery with group <inline-formula> <tex-math>$ell _{1}$ </tex-math></inline-formula>-regularization for sidelobe suppression. Through an ablation study, we substantiate that the proposed successive unrolled network outperforms the conventional unrolled network in terms of both magnitude and phase estimation accuracy. Moreover, we present how the proposed successive network robustly scales to large MIMO configurations (up to 32 transmit antennas), where the conventional methods tend to fail. Successive learned FISTA (L-FISTA) achieves a dynamic range of 99.5 dB for a <inline-formula> <tex-math>$32times 4$ </tex-math></inline-formula> PMCW radar. Additionally, the methods are evaluated at various levels of sparsity, using range-Doppler maps (RD maps) in dense target scenarios. Finally, we compare the computational load of the presented methods using the floating-point operations (FLOPs) metric.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1350-1361"},"PeriodicalIF":0.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405306","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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