IEEE Transactions on Radar Systems最新文献

{"title":"Physics-Guided Learnable Wavelet Framework for Micro-Doppler Characterization of Low-Altitude Radar Targets","authors":"Xiaolong Chen;Jinhao Wang;Zhijian Zhao;Jian Guan;Xiaolin Du;Yifeng Wu;Hai Zhang","doi":"10.1109/TRS.2026.3738487","DOIUrl":"https://doi.org/10.1109/TRS.2026.3738487","url":null,"abstract":"Reliable micro-Doppler characterization is relevant to low-altitude radar sensing, where measured echoes may contain weak returns. Fixed-parameter time–frequency methods can produce smeared or fragmented time–frequency representations (TFRs), which can affect weak-component observability and Doppler-ridge traceability. This article presents WConV-TF, a physics-guided learnable wavelet front end for radar micro-motion analysis. WConV-TF converts preprocessed target-range-cell echoes into micro-Doppler TFRs by embedding parameterized Morlet bases in a learnable complex wavelet convolution layer. The normalized center frequencies and bandwidths are optimized from data while retaining a time–frequency interpretation. A multiscale residual module then refines the magnitude-domain TFR. The method is evaluated using simulated data, measured frequency-modulated continuous-wave (FMCW) radar data, and measured S-band array radar data. Across four measured target categories, WConV-TF reduces Rényi entropy by approximately 0.4%–46.3% relative to the strongest competing method for each category and reduces the controlled-rotor mean absolute localization error (MALE) by approximately 66.9% relative to the best baseline. On the common four-category sequence-level split, it improves downstream classification accuracy by 4.66 percentage points over short-time Fourier transform (STFT). The results across simulated signals, two measured radar systems, and four target types indicate applicability to low-altitude radar signal analysis under the evaluated protocols.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1678-1691"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133407","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":"Doppler-Domain Warping and Resampling for Semi-Static Human Occupancy Detection Using Low-Resolution SIMO FMCW Radar","authors":"Huy Trinh;Elliot Creager;George Shaker","doi":"10.1109/TRS.2026.3680942","DOIUrl":"https://doi.org/10.1109/TRS.2026.3680942","url":null,"abstract":"Radar-based human activity recognition is a promising privacy-preserving alternative to vision and wearable sensors, especially in sensitive environments such as long-term-care (LTC) facilities. However, reliable detection of quasi-static human presence, where subtle micromotions such as small postural sway, breathing, small limb readjustments, etc., concentrate their energy around near-zero Doppler using low-resolution radar, remains challenging. In this work, we propose range-angle-based semi-static occupancy (RASSO), an invertible Doppler-domain nonlinear remapping layer that resamples the slow-time fast Fourier transform (FFT) (Doppler) axis to densify the sampling grid around near-zero Doppler before adaptive Capon beamforming. This produces range-azimuth (RA) feature maps with higher signal-to-noise ratios (SNRs), resulting in sharpened target peaks and reduced background variance. On a real dataset collected in a nursing home using short-range single-input and multiple-output (SIMO) frequency-modulated continuous-wave (FMCW) radar, RASSO consistently yields higher contrast RA maps that are easier to threshold and more informative for downstream models. Under cell-averaging constant false alarm rate (CA-CFAR), RASSO-enhanced RA (RASSO-RA) achieves the highest detection-curve area under the curve (AUC = 0.981) and the best recall at false alarm rates (FARs) of 1% and 5% (0.920 and 0.947), compared to conventional Capon beam formation and a recent state-of-the-art baseline. Training on RASSO-RA further improves data-driven methods: a frame-based SimpleCNN reaches 95%–99% accuracy across subjects, while a sequence-based convolutional neural network (CNN)-long short-term memory (LSTM) achieves 99.4%–99.6% accuracy, with paired session-level bootstrap analysis confirming statistically significant macro-F1 improvements of 2.6–3.6 percentage points (95% confidence intervals strictly above zero) over the nonwarped baseline.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"855-870"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796034","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":"CFAR Detection of High-Accelerating Targets: A Study of the Probability of False Alarm","authors":"Alessio Balleri;Fabiola Colone;Pierfrancesco Lombardo","doi":"10.1109/TRS.2026.3679015","DOIUrl":"https://doi.org/10.1109/TRS.2026.3679015","url":null,"abstract":"Fast accelerating targets feature a nonnegligible time-varying Doppler shift which induces a drop in signal-to-noise ratio (SNR) at the output of a classical range-Doppler radar detector. The optimal detector against fast accelerating targets retrieves the maximum available SNR by a 3-D search to match the target echo to the target range, Doppler, and acceleration. This operation, however, modifies the statistical properties probability density function (p.d.f) and the correlation) of background white Gaussian noise at the output of the filter and, ultimately, impacts the receiver probability of false alarm and how this can be robustly controlled. In this article, we study the statistical properties of white Gaussian background noise at the output of the optimal processor and propose a modified one-parametric largest exponential value (LEV) distribution to fit the receiver noise before the detection threshold. Two constant false alarm rate (CFAR) schemes are proposed and developed based on the modified LEV distribution and their performance is characterized analytically and compared with simulated data and experimental data collected at C-band. Results show that the distribution parameter of the modified LEV converges to a fixed value as the number of filters in the acceleration domain increases. This yields detection performance that is independent of the relevant detection parameters. CFAR results demonstrate that the probability of false alarm can be robustly controlled with the proposed solution on simulated and real experimental data.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"711-722"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11457922","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606302","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":"Optimizing Dual-Doppler Mobile Radar Deployment With an Obstacle-Aware Backup Coverage Location Model for Strategic Storm Data Collection","authors":"Chenchun Wang;Xin Feng;A. Addison Alford","doi":"10.1109/TRS.2026.3682657","DOIUrl":"https://doi.org/10.1109/TRS.2026.3682657","url":null,"abstract":"Reliable radar observations are essential for enhancing our comprehension and prediction of hazardous and economically significant severe weather phenomena. Mobile radars have demonstrated efficacy in enhancing the modeling and conceptual understanding of severe weather phenomena by providing high spatial and temporal resolution observations. A critical problem with mobile radar observations is finding ideal deployment locations. Despite the scientists’ experience in radar functionality and the local terrain environment, they encounter considerable challenges in selecting the appropriate location for meteorological observation. They need to account for the acquisition of extensive, usable data for rapidly changing severe weather events and guarantee the safety of operators and vehicles. A thorough understanding of the surrounding environment, including road networks, topography, vegetation, structures, and other obstacles, is crucial for the real-time deployment of mobile radar vehicles. Nonetheless, on-site assessments require substantial labor, material resources, and sufficient time with which to evaluate potential radar site locations. This article utilizes an obstacle-aware backup coverage location model to determine deployment locations for two mobile radar vehicles in a dual-Doppler configuration. We validate the model performance using the 2004 Geary tornado event in Oklahoma. The findings of the application demonstrate the effectiveness, efficiency, and flexibility of ground-based mobile radar deployment via an integrated spatial analytical framework.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"841-854"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737148","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":"An Isometry Metric for Waveform Assessment in the Presence of Correlated Interference","authors":"Callin A. Schone;Nathan A. Goodman","doi":"10.1109/TRS.2026.3675552","DOIUrl":"https://doi.org/10.1109/TRS.2026.3675552","url":null,"abstract":"The restricted isometry property (RIP) is used in compressive sensing (CS) to assess sensing performance. In this article, we apply a RIP-inspired metric as a novel technique for quantifying radar waveform performance. While the RIP has importance with respect to CS, its application has been limited to compressed measurements corrupted by uncorrelated interference. Active radar systems, however, are often expected to detect signals in the presence of waveform-dependent correlated interference, such as ground clutter. Therefore, we introduce a RIP-inspired isometry metric (IM) that accounts for correlated interference via a whitening transformation. This modification of the RIP accounts for the waveform-dependent nature of received signals and clutter, which then enables its use as a quantitative waveform performance metric. The proposed IM can handle nonuniform pulse repetition intervals and other diversity techniques used to mitigate range–Doppler ambiguities. The benefits of the proposed metric are demonstrated through various simulated examples that yield intuitive and quantitative waveform comparisons.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"745-758"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696715","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":"Spectral Micro-Unit–Based Insect–Bird Classification Using Polarimetric Weather Radar","authors":"Mingming Ding;Cheng Hu;Kai Cui;Rui Wang;Zujing Yan;Zhongbo Liu;Dongli Wu;Wei Zhang","doi":"10.1109/TRS.2026.3705469","DOIUrl":"https://doi.org/10.1109/TRS.2026.3705469","url":null,"abstract":"Insect–bird classification using weather radar is a key component of large-scale aeroecological monitoring and supports applications such as bird-strike risk warning and pest surveillance. However, conventional radar-based products describe only bulk properties of radar resolution cells and fail to capture fine-scale internal structures, limiting accurate classification when insects and birds coexist within the same cell. To address this limitation, this study proposes an insect–bird classification method based on spectral micro-unit features derived from weather radar spectral signals. Spectral micro-units associated with biological scatterers are extracted through spectral peak matching between horizontal and vertical polarization channels. Based on differences in morphology and flight behavior between insects and birds, a spectral micro-unit feature system is constructed to characterize intensity, structural dispersion, and symmetry. An unsupervised feature-space clustering approach is then employed to achieve insect–bird classification without manual labeling. Experimental results demonstrate that the proposed method achieves a clustering silhouette coefficient (SC) of 0.703, effectively separating insects and birds and enabling reliable identification of mixed insect–bird targets within individual radar resolution cells. The proposed method establishes a spectral-level analytical framework for insect–bird classification and provides a reference for future fine-grained classification at higher taxonomic levels.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1167-1180"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443222","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-Altitude Drone Monitoring Using Millimeter-Wave Cellular Base Stations and Covariance-Aware Multiradar Fusion","authors":"Dhruva Anantha Datta;Sean Peters","doi":"10.1109/TRS.2026.3716138","DOIUrl":"https://doi.org/10.1109/TRS.2026.3716138","url":null,"abstract":"Low-altitude drones pose significant challenges for airspace safety and navigation, often requiring dedicated radar systems for monitoring. Commercial millimeter-wave cellular base stations provide dense urban infrastructure, large instantaneous bandwidths, directive antenna arrays, and software-defined processing chains that can support radar-like sensing. In particular, fifth-generation (5G) New Radio (NR) frequency range 2 (FR2) hardware provides bandwidths up to 400 MHz and directive massive multiple-input–multiple-output (MIMO) apertures that could enable submeter range resolution and multibeam tracking using existing sites. Here, we investigate the feasibility of reusing commercial millimeter-wave cellular base stations as a distributed radar network for low-altitude uncrewed aerial vehicle (UAV) monitoring. We derive power signal-to-noise-ratio (SNR) bounds, quantify spatiotemporal resolutions, and discuss clutter and Doppler phenomenology for dense urban scenes. We develop a covariance-aware information-space fusion algorithm that combines bearing, elevation, and range observations from multiple stations. The proposed method rotates local measurement covariances into a global Cartesian frame and accumulates information using SNR gating, variance floors, and Huber reweighting for outlier suppression. Monte Carlo trials compare fused multiradar positioning against the Global Positioning System (GPS) under both global navigation satellite system (GNSS)-available and GNSS-denied conditions. Our results show that, in typical line-of-sight (LoS) FR2 deployment settings, fused millimeter-wave cellular sensing can achieve error distributions competitive with the GPS comparator and can outperform it under GNSS-degraded conditions. We then outline a 5G NR-specific implementation path in which the baseband/field-programmable gate array (FPGA) stack schedules sensing bursts in time-division duplexing (TDD) idle slots and returns fused positions to cooperative UAVs. This approach presents a promising avenue for real-time drone monitoring using existing millimeter-wave cellular infrastructure while minimizing additional hardware deployment. We conclude by discussing the implementation challenges and next steps for field validation.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1361-1376"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148748003","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":"Robust Angle Estimation Within the Single Beamwidth Based on Nonlinear Wavefront Modulation and Sparse Recovery","authors":"Yuepeng Wu;Mengyang Shi;Yesheng Gao;Bin Yuan;Xingzhao Liu","doi":"10.1109/TRS.2026.3720479","DOIUrl":"https://doi.org/10.1109/TRS.2026.3720479","url":null,"abstract":"Accurate angle estimation of targets within a single beamwidth is highly desirable in radar applications. This article proposes a robust angle estimation method based on nonlinear wavefront modulation (NWM) and sparse recovery. First, a parameterized model is developed to reformulate the angle estimation problem as a linear inverse problem. The primary innovation lies in the introduction of NWM to ensure the solvability of the equations. To address the ill-posedness arising from high correlation between columns of the wavefront modulation matrix, sparse constraints are incorporated to stabilize the solution. Specifically, this work introduces the regularized modified sparse Bayesian learning (RM-SBL) algorithm for high-precision angle estimation under an additive white Gaussian noise (AWGN) model. The stabilizing mechanism of these sparse constraints is evaluated through the lenses of both Cramér–Rao bound (CRB) analysis and regularization theory. Finally, comprehensive implementation details are provided, encompassing system design, experimental setup, and the signal processing workflow. Experimental results demonstrate an angular root-mean-square error (RMSE) of 0.195°, which is equivalent to approximately 1/80 of the antenna beamwidth (16°). Consequently, high-precision angle estimation can be achieved through noninvasive hardware enhancements to existing system configurations. The method also demonstrates consistent robustness across varying signal-to-noise ratios (SNRs), different numbers of wavefront modulation states, calibration inaccuracies, and diverse target amplitude distributions. This work contributes to the advancement of wavefront modulation radar systems and exhibits significant potential to enhance angular estimation accuracy in practical deployments.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1395-1414"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148748049","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 Single-Channel Wide-Beam Radar Angle Estimation Accuracy Improvement Method by Nonlinear Wavefront Modulation","authors":"Mengyang Shi;Yuepeng Wu;Yesheng Gao;Wenxuan Shi;Bin Yuan;Xingzhao Liu","doi":"10.1109/TRS.2026.3687266","DOIUrl":"https://doi.org/10.1109/TRS.2026.3687266","url":null,"abstract":"Target angle estimation with a single-channel wide-beam antenna is a fundamental and crucial task in radar and remote sensing. Traditional target angle estimation methods primarily rely on the phase differences of electromagnetic waves between array antennas, resulting in a high level of complexity in the antenna system. Furthermore, the angle estimation accuracy is also limited by the array antenna aperture. To alleviate the problems of system complexity and aperture limitations, a multimedia nonlinear wavefront modulation system (MN-WMS) for angle estimation is proposed. This system does not require modification of the original wide-beam antenna; therefore, the system complexity is low. By introducing nonlinear wavefront modulation information, the target angle can be accurately estimated. Specifically, we first propose a wavefront modulation angle estimation principle that introduces additional reference information for the angle estimation task. Then, we propose a multimedia modulator for electromagnetic waves modulation and present the implementation of the modulator. Finally, a multimedia nonlinear wavefront modulation angle estimation algorithm (MN-WMA) is proposed, which can be adaptively adjusted to balance processing time and accuracy requirements. High-accuracy angle estimation for a single-channel wide-beam antenna can be effectively realized. Microwave anechoic chamber experiments show that the method achieves an angle measurement accuracy of <inline-formula> <tex-math>$0.163{mathrm {^{circ } }}$ </tex-math></inline-formula> with the single-channel wide-beam antenna, verifying the effectiveness of the proposed method. This is a new exploration of using the nonlinear wavefront modulation method for angle estimation with a single-channel wide-beam antenna.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"911-924"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937617","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":"MVLSN-SegNet: Multifeature View-Based Land–Sea–Noise Segmentation Network for Maritime Surveillance Radars","authors":"Jian Xue;Zhuo Feng;Shuwen Xu;Meiyan Pan","doi":"10.1109/TRS.2026.3691022","DOIUrl":"https://doi.org/10.1109/TRS.2026.3691022","url":null,"abstract":"Land–sea–noise segmentation is essential for maritime surveillance radars, particularly in short-pulse search mode. Traditional methods focus on land–sea segmentation using single-feature information, and typically rely on morphological filtering and manual parameter tuning. To increase segmentation of noise areas and reduce the number of hyperparameters, this article proposes a multifeature view-based land–sea–noise segmentation network (MVLSN-SegNet) for scenario perception of maritime surveillance radars. First, four feature views from radar echoes (average power (AP), phase linearity degree (PLD), energy concentration degree (ECD), and direct-current component ratio) are extracted to fully utilize discriminative information captured from different views. Then, the MVLSN-SegNet is designed for land–sea–noise segmentation via improving an overview-first-look-closely next convnet with a local–global attention (LGA), long-range skip connection (LSC), and a hybrid loss function. Experimental results on real X-band maritime radar data show that the proposed MVLSN-SegNet achieves a mean intersection over union (mIOU) of 96.19%, outperforming the baselines (U-Net, PSPNet, SegFormer, DeepLabv3+, and MobileNetV3-L).","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"943-955"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003536","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}