IEEE Transactions on Radar Systems最新文献

{"title":"Array Gain for Digital Beamforming Effects of Distributed Targets and Quantization","authors":"Ayano Ueki;Robert D. Palmer;Boonleng Cheong;Sebastián M. Torres","doi":"10.1109/TRS.2026.3734006","DOIUrl":"https://doi.org/10.1109/TRS.2026.3734006","url":null,"abstract":"Fully digital phased-array radars (PARs) have several advantages over conventional architectures, including flexible multibeam steering, adaptive beamforming, and signal-to-noise ratio (SNR) improvement in array gain due to the independence of receiver noise across array elements. However, the resulting data volume poses a significant bottleneck for real-time operation, requiring substantial data transfer bandwidth, processing resources, and storage capacity. In this study, uniform quantization is applied at the analog-to-digital converter (ADC) to reduce the data volume of element-level I/Q signals in the amplitude domain, and its impact on array gain is investigated through numerical simulation. Specifically, the analysis is conducted from two aspects: 1) array gain as a function of target distribution and 2) array gain as a function of quantization bit level. The results demonstrate that array gain is governed by the spatial correlation of the targets, indicating that it must be evaluated with respect to target distribution even with minimal quantization effects. Furthermore, the spatial characteristics of quantization noise are shown to depend on the steering direction. In simulations, the mismatch between the quantized projection weights and the beamforming weights reduces the correlation of quantization noise, resulting in its incoherent summation during beamforming and mitigating its impact on the beamformed output. Moreover, the spatial correlation of quantization noise across the array dominates the array gain characteristics as a function of steering angle.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1592-1605"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11693972","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133426","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}

{"title":"Complex-Valued Hierarchical Fusion Network for Single-Frame Cross-Range Velocity Estimation With Millimeter-Wave MIMO Radar","authors":"Bochuan Wu;Xiaodong Zhuge","doi":"10.1109/TRS.2026.3737716","DOIUrl":"https://doi.org/10.1109/TRS.2026.3737716","url":null,"abstract":"Conventional frequency-modulated continuous-wave (FMCW) processing directly measures radial velocity but does not provide the cross-range component from a single Doppler peak. This limitation constrains real-time motion sensing in short-range applications, especially in automotive traffic scenarios involving pedestrians and vehicles. This article investigates whether weak motion-induced curvature and joint chirp–antenna phase structure can support single-frame estimation of the 2-D in-plane velocity vector with a compact 24-GHz 2Tx–4Rx multiple-input multiple-output (MIMO) radar. A hierarchical dual-branch network is proposed: a position branch processes a complex range–angle (RA) map for localization, while a velocity branch processes complex range–velocity (RV) and velocity–angle (VA) maps together with a magnitude range–Doppler (RD) map for kinematic estimation, followed by a transformer decoder for set-based multitarget prediction. On simulated multitarget scenarios at short range, the method achieves an <inline-formula> <tex-math>$F1$ </tex-math></inline-formula>-score of 0.959 (full held-out test set, gate-2.0 global Hungarian matching) with root-mean-square errors (RMSEs) of 0.64 and 1.05 m/s for radial and cross-range velocity, respectively, and an <inline-formula> <tex-math>$F1$ </tex-math></inline-formula>-score of 0.904 with a cross-range RMSE of 1.894 m/s on a frame-disjoint, within-session real-world pedestrian evaluation. On the same real evaluation, the sign-error rate is 19.8% and the <inline-formula> <tex-math>$vert $ </tex-math></inline-formula>vcr<inline-formula> <tex-math>$vert $ </tex-math></inline-formula> RMSE is 1.52 m/s; an independent simulated diagnostic evaluation (not the primary <inline-formula> <tex-math>$F 1=0.959$ </tex-math></inline-formula> result) yields 7.5% and 0.86 m/s. These results indicate that cross-range velocity is estimable from a single frame with a compact array. Detection accuracy decreases as the number of targets increases in the evaluated multitarget scenarios. Across the evaluated 0–20 m range, cross-range RMSE remains 1.04–1.14 m/s, while the simulated sign-error rate increases from 2.8% at 0–5 m to 13.3% at 15–20 m.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1692-1705"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133763","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}

{"title":"Amplitude-Square CFAR Detection in K-Distributed Sea Clutter","authors":"Nagarakshith Mungara;Alessio Balleri;Tim Acland","doi":"10.1109/TRS.2026.3737370","DOIUrl":"https://doi.org/10.1109/TRS.2026.3737370","url":null,"abstract":"This article investigates constant false alarm rate (CFAR) detection for radars employing amplitude-square detectors, with the aim of controlling false alarms in confused choppy waters, typical of coastal shallow areas. CFAR amplitude-square detectors are analytically demonstrated for K-distributed clutter with fully correlated and statistically independent texture. It is shown analytically that the cell averaging CFAR (CA-CFAR) detector for fully correlated texture exhibits complete CFAR behavior against all clutter distribution parameters. A CA-CFAR detector and an order statistics CFAR (OS-CFAR) detector are developed for the case of statistically independent K-distributed clutter, and it is shown analytically that these are CFAR only with respect to the clutter scale parameter. The performance of all proposed CFAR detectors is demonstrated and evaluated with simulations, and their robustness against parameter estimation errors and model mismatches is also analyzed. The detectors are finally evaluated and compared with other existing solutions, against experimental data collected with the Hensoldt Mk 11 SharpEye X-band Radar in the English Channel shallow waters. Simulation results corroborate the CFAR behavior of the detectors. The experimental results show that, on our dataset, the proposed detectors can be used to better control the probability of false alarm (PFA) with respect to classical Gaussian CA-CFAR and OS-CFAR detectors and other existing CFAR detectors developed for Weibull, Gamma, and Pareto distributed clutter.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1706-1725"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133873","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}

{"title":"A Low-Complexity Denoising and Fine Estimation Algorithm for MIMO Radar","authors":"Yanpeng Su;Norman Franchi;Maximilian Lübke","doi":"10.1109/TRS.2026.3677521","DOIUrl":"https://doi.org/10.1109/TRS.2026.3677521","url":null,"abstract":"This article proposes a multiple-input multiple-output (MIMO) radar denoising and fine estimation (DFE) algorithm. The DFE algorithm enables fine parameter estimation and efficient cancellation of the detected reflection signals, thereby providing a cleaner range-Doppler map and mitigating the intertarget interference. Further, we introduce a code-space-orthogonal phase-modulated continuous waveform (CSO-PMCW) integrated sensing and communications (ISAC) system, which achieves much higher throughput than conventional PMCW while inheriting its advantages in radar and antiinterference performance. The DFE algorithm is integrated into the CSO-PMCW system, and its applicability to other radar waveforms is explained. Simulation results demonstrate that the DFE algorithm can achieve at least a tenfold improvement in range, velocity, and angular accuracy without significantly increased computational burden.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"690-696"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606301","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}

{"title":"Ship Classification by Cyclostationary Analysis of Preimage Formation ISAR Data","authors":"James Lu;Jeffrey Krolik","doi":"10.1109/TRS.2026.3706300","DOIUrl":"https://doi.org/10.1109/TRS.2026.3706300","url":null,"abstract":"Inverse synthetic aperture radar (ISAR) has proven valuable for imaging in maritime contexts. However, micro-Doppler (m-D) effects from rotating components on ships often blur ISAR imagery and complicate classification. On the other hand, m-D measurement carries unique characteristics that can distinguish specific vessel types with shared structural features. This article introduces a cyclostationary m-D framework for ship classification that fundamentally differs from prior approaches by: 1) extracting m-D features before ISAR image formation; 2) recovering physically interpretable micromotion parameters; and 3) relaxing the need for strictly sinusoidal motion models. We validate the proposed approach through a hierarchy of simulation experiments, estimating rotators’ unique locations, frequencies, and radii. The proposed approach demonstrates improved performance over traditional m-D estimation techniques such as short-time Fourier transform (STFT) in noisy environments. Our findings illustrate the robust extraction of m-D features for accurate ship classification in challenging maritime environments.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1157-1166"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443138","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}

{"title":"Bayesian Multipath Mitigation Technique With Fully Digital Phased-Array Radar","authors":"Biagio Ianero;Alfonso Farina","doi":"10.1109/TRS.2026.3700899","DOIUrl":"https://doi.org/10.1109/TRS.2026.3700899","url":null,"abstract":"Multipath propagation represents a major source of bias in angle-of-arrival estimation for pulsed radar systems, particularly in low-elevation scenarios. Classical techniques, such as monopulse processing, are known to exhibit systematic errors in the presence of multipath reflections. This article proposes a Bayesian antimultipath approach that jointly estimates the angle of arrival of the target of interest and the angle of arrival of the multipath component, explicitly exploiting a spherical Earth geometry and a four-ray multipath propagation model. In addition to angle estimation, the proposed method enables the separate estimation of the received signal power associated with aggregated signal contributions arriving from above and below the radar horizon. The performance of the estimator is evaluated through numerical simulations and compared with classical monopulse processing and the complex angle (CA) technique under realistic multipath conditions. Results show that the proposed Bayesian estimator substantially reduces the bias in the elevation error in the presence of multipath, whereas monopulse-based angle estimates and CA estimates exhibit significant bias or instability, demonstrating the effectiveness of the proposed approach for robust angle estimation in multipath environments.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1059-1072"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433348","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}

{"title":"Corrections to “TD-AMRKNet-Based Radar Image Processing Framework for Sea Clutter Suppression”","authors":"Xiaolin Du;Di Ma;Xiaolong Chen;Guolong Cui;Jibin Zheng","doi":"10.1109/TRS.2026.3696468","DOIUrl":"https://doi.org/10.1109/TRS.2026.3696468","url":null,"abstract":"In the above article [1], after publication, the authors identified several inadvertent figure-related errors. The corrections are as follows. The scope of this correction is limited to replacing two incorrectly placed qualitative result figures and correcting two interchanged figure captions. These changes do not affect the experimental data, numerical results, comparative analysis, or conclusions of the article.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"999-999"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11547318","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171576","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}

{"title":"Extended Object Tracking Algorithms Within Interacting Multiple Model Framework for Automotive Radar Applications","authors":"Sanglap Sarkar;Anirban Roy","doi":"10.1109/TRS.2026.3718282","DOIUrl":"https://doi.org/10.1109/TRS.2026.3718282","url":null,"abstract":"This article presents a novel computationally efficient extended target tracking (ETT) framework for automotive radar based on the interacting multiple model (IMM) algorithm. First, the detailed mathematical foundation of a new variant of a multihypothesis tracking algorithm for extended object tracking named centroid-oriented multihypothesis centroid-oriented MHT (Co-MHT) is developed, in which confirmed measurement clusters obtained after hypothesis pruning are used to construct an equivalent kinematic observation together with a spatial scatter representation. This cluster-level formulation enables joint estimation of target kinematics and physical extent, while eliminating hypothesis tree growth and significantly reducing runtime and memory requirements compared with classical IMM track-oriented multihypothesis tracking (To-MHT). The framework incorporates constant-velocity (CV) and constant-turn-rate-and-acceleration (CTRA) motion models and supports robust estimation of object orientation and dimensions using detection clustering and statistical extent modeling. The second contribution of this article is to propose a detailed mathematical foundation of a new variant of the traditional probabilistic data-association filter (PDAF) named the skew-adaptive PDAF (SA-PDAF) to address the limitations of conventional probabilistic data association (PDA) in extended target scenarios. High-resolution automotive radars often generate multiple, nonuniform reflections from different parts of an object, resulting in highly skewed association probabilities that lead to centroid drift and track fragmentation. The proposed SA-PDAF algorithm applies a novel skew-adaptive transformation to the association probabilities, thereby suppressing imbalance, stabilizing centroid estimation, and improving robustness against object fragmentation. Extensive on-road vehicle experiments and offline performance evaluation using optimal sub-pattern assignment (OSPA) metric demonstrate that both IMM-based Co-MHT and the SA-PDAF framework achieve stable tracking performance, accurate kinematic and extent estimation, and superior robustness under real-life extended target conditions.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1415-1425"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148748050","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}

{"title":"Bridging Simulation and Reality: Modeling Wind-Driven Micro-Doppler Variability in UAV Radar Signatures","authors":"Megha Kataria;Brejesh Lall","doi":"10.1109/TRS.2026.3650797","DOIUrl":"https://doi.org/10.1109/TRS.2026.3650797","url":null,"abstract":"Understanding the impact of environmental disturbances on radar micro-Doppler signatures is critical for the reliable detection, classification, and tracking of small aerial targets such as quadcopters. In real-world operations, wind-induced motion is inevitable and can distort the ideal micro-Doppler signatures typically generated through simulations, as collecting high-quality real-world radar returns is often challenging due to logistical and safety constraints. This study investigates the influence of wind-like perturbations on the radar return from a hovering quadcopter by modeling its translational velocity as an Ornstein–Uhlenbeck (OU) stochastic process—a physically plausible representation of temporally correlated wind-induced drift. Through comparative analysis of clean (ideal hovering) and wind-induced (nonideal) cases, we observe significant deviations in spectral characteristics, including energy broadening and raised spectral floors in the power spectral density (PSD), degradation of harmonic structures in scalograms and in persistence spectrums, and increased spectral centroid and spectral spread fluctuations over time. These findings demonstrate that even moderate wind-induced drift can introduce ambiguity in signature-based drone classification systems. By providing a controlled yet realistic simulation framework for wind effects, this work offers key insights into the design of robust radar-based drone monitoring systems intended for deployment in complex, real-world environments.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"321-337"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148478808","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}

{"title":"Low-Complexity Frequency-Hopped Method for ISAC Systems","authors":"Robert S. C. Winter;Aled Catherall;Christos Masouros;Matthew Ritchie","doi":"10.1109/TRS.2026.3691582","DOIUrl":"https://doi.org/10.1109/TRS.2026.3691582","url":null,"abstract":"This study explores a low-complexity approach to frequency hopping for integrated sensing and communication (ISAC) systems. We propose to break up chirps of frequency-modulated continuous-wave (FMCW) radar into discrete sections in order to build a communication constellation. This technique is motivated and analyzed from a theoretical standpoint. It is then experimentally demonstrated using a radio frequency system-on-chip (RFSoC) software-defined radio (SDR). Our approach allows data rates of up to several megabits per second (Mbps) while requiring minimal additional computational overhead to perform the recovery of the sensing information. Any artifacts introduced by this modality are also shown to be negligible at lower data rates. The proposed approach benefits from a similar architecture to LoRa waveforms and has the same high tolerance for noise. Our simulations and experiments show that this technique provides higher data rates with negligible computational overhead when compared to other radar-centric ISAC techniques. It also has a much more realistic roadmap toward implementation due to the ease of demodulation of both communication and sensing waveforms. Overall, this technique shows promise for adding low data-rate communications to FMCW radar with minimal cost to the primary function of sensing.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1073-1083"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433133","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}