IEEE Transactions on Radar Systems最新文献_第7页

Tools and Analysis for Distributed SAR Systems 分布式SAR系统的工具和分析

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-04-03 DOI: 10.1109/TRS.2026.3680763

Greta Zefi;Finlay Rollo;Christos Ilioudis;Carmine Clemente

{"title":"Tools and Analysis for Distributed SAR Systems","authors":"Greta Zefi;Finlay Rollo;Christos Ilioudis;Carmine Clemente","doi":"10.1109/TRS.2026.3680763","DOIUrl":"https://doi.org/10.1109/TRS.2026.3680763","url":null,"abstract":"Spaceborne synthetic aperture radar (SAR) is a crucial sensor for wide-area Earth observation, playing a pivotal role in terrain mapping, ocean monitoring, and disaster management. To meet the growing demand for diverse applications and improved performance, and enabled by advances in satellite and radar technologies, distributed configurations have emerged as a key approach in the development of spaceborne SAR. This article presents a full analytical characterization of distributed SAR systems. To the best of our knowledge, the closed-form expressions for azimuth and ground-range resolution in distributed SAR presented in this work are derived for the first time. Mathematical derivations of the azimuth and range resolution are provided, and the case of bandwidth overlap in azimuth resolution is also considered, together with a detailed characterization of iso-Doppler and iso-range lines. Among the various geometric configurations analyzed, particular attention is devoted to the symmetric case, where receivers are arranged symmetrically with respect to the transmitter. In this configuration, the iso-Doppler and iso-range contours converge to those of the equivalent monostatic SAR geometry.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"883-896"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796210","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}

引用次数: 0

Constrained Deep Reinforcement Learning for Cognitive Radar Resource Management 认知雷达资源管理的约束深度强化学习

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-03-02 DOI: 10.1109/TRS.2026.3669518

Ziyang Lu;M. Cenk Gursoy;Chilukuri K. Mohan;Pramod K. Varshney

{"title":"Constrained Deep Reinforcement Learning for Cognitive Radar Resource Management","authors":"Ziyang Lu;M. Cenk Gursoy;Chilukuri K. Mohan;Pramod K. Varshney","doi":"10.1109/TRS.2026.3669518","DOIUrl":"https://doi.org/10.1109/TRS.2026.3669518","url":null,"abstract":"In this article, multitarget tracking and scanning are considered in a radar system operating in the track-while-scan mode. Specifically, time allocation for radar scanning and tracking of multiple maneuvering targets under a time budget constraint is addressed, aiming to jointly optimize the performance of both tracking and scanning in a cognitive radar. We first present the details of the model for tracking and scanning and formulate the time management task as a constrained optimization problem. Subsequently, we design a constrained deep reinforcement learning (CDRL) framework to find the time allocation strategy for the problem. In the proposed CDRL framework, the parameters of the neural networks and the dual variable are learned simultaneously. The deep deterministic policy gradient (DDPG) algorithm is introduced to tackle continuous action space and its performance is compared with deep Q-learning (DQL), heuristic approaches, and an optimization-based approach. Numerical results show that the radar with the proposed CDRL framework can autonomously allocate more time to the tracking task that requires greater attention while providing time for scanning and also constraining the total time budget below the predefined threshold.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"627-644"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557389","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}

引用次数: 0

Moving Target Detection Using Passive Bistatic Radars in Presence of Interfering Target and I/Q Imbalances 无源双基地雷达在干扰目标和I/Q不平衡情况下的运动目标检测

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-01-28 DOI: 10.1109/TRS.2026.3658846

Hossein Nikaein;Mohammad Reza Jabbari;Saeed Gazor

{"title":"Moving Target Detection Using Passive Bistatic Radars in Presence of Interfering Target and I/Q Imbalances","authors":"Hossein Nikaein;Mohammad Reza Jabbari;Saeed Gazor","doi":"10.1109/TRS.2026.3658846","DOIUrl":"https://doi.org/10.1109/TRS.2026.3658846","url":null,"abstract":"This article addresses moving target detection in the presence of an interfering target (IT) using passive bistatic radar (PBR) systems affected by hardware mismatches. We focus on in-phase/quadrature-phase imbalances (IQIs) common in direct-conversion receivers (DCRs), where IT echoes mask the main target (MT). When unmitigated, both effects significantly degrade detection performance. Unlike existing approaches, we assume unknown noise power and unknown target Doppler shifts (DSs), further increasing the detection challenge. We develop a new signal model for this scenario, formulate a binary composite hypothesis testing (BCHT) problem, and derive a closed-form asymptotically optimal generalized likelihood ratio test (GLRT) detector using the Neyman-Pearson criterion. To improve computational efficiency, we exploit fast Fourier transform (FFT) symmetry properties and employ batch data processing, substantially reducing computational complexity (CC) and execution time. We also analyze the impact of amplitude and phase mismatches on the PBR signal model. Theoretical analysis and simulations demonstrate improved detection probability (DP) and enhanced robustness to IQI compared with state-of-the-art methods.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"586-596"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557409","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}

引用次数: 0

Message Passing-Based Robust Asynchronous Multistatic Localization Using Differential Time Delays With Unknown Transmitter Position 基于消息传递的未知发射机位置差分时延鲁棒异步多静态定位

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-04-07 DOI: 10.1109/TRS.2026.3681815

Wu Hong;Ming Jin;Qinghua Guo;Wenjuan Li

引用次数: 0

Radar Mounting Angle Estimation in Operational Driving Conditions 实际驾驶条件下雷达安装角估计

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-06-01 DOI: 10.1109/TRS.2026.3698943

Simin Zhu;Satish Ravindran;Lihui Chen;Alexander G. Yarovoy;Francesco Fioranelli

{"title":"Radar Mounting Angle Estimation in Operational Driving Conditions","authors":"Simin Zhu;Satish Ravindran;Lihui Chen;Alexander G. Yarovoy;Francesco Fioranelli","doi":"10.1109/TRS.2026.3698943","DOIUrl":"https://doi.org/10.1109/TRS.2026.3698943","url":null,"abstract":"The problem of estimating the mounting angle of millimeter-wave (mmWave) automotive radars installed on moving vehicles is investigated. We address this angle estimation problem during normal driving, without relying on controlled environments, dedicated radar targets, or specially designed driving routes. To achieve this, we propose a signal processing pipeline that combines radar and inertial measurement unit (IMU) data to enable accurate and reliable estimation under realistic driving conditions. Unlike previous studies, the method employs neural networks (NNs) to process sparse and noisy radar measurements, reject detections from moving objects, and estimate radar motion. In addition, a measurement model is introduced to correct IMU bias and scale factor errors. Using vehicle kinematics, the radar mounting angle is then computed from the estimated radar motion and the vehicle’s yaw rate. To benchmark performance, the proposed approach is comprehensively compared with two alternative problem formulations and four estimation techniques reported in the literature. Validation is carried out on the challenging RadarScenes dataset, covering over 79 km of real-world driving with different velocities and trajectories. The results show that stable and accurate mounting angle estimates are obtained within approximately 25 s of driving. To the best of the authors’ knowledge, this is the first study to demonstrate that automotive radar mounting angles can be estimated during complex, real traffic conditions using only onboard sensor data.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1041-1051"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148235904","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}

引用次数: 0

Breaking the CP Limit: Robust Long-Range OFDM Sensing via Interference Cleaning 突破CP限制：通过干扰清除的鲁棒远程OFDM传感

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-06-15 DOI: 10.1109/TRS.2026.3704011

Umut Utku Erdem;Lucas Giroto;Benedikt Geiger;Taewon Jeong;Silvio Mandelli;Christian Karle;Benjamin Nuss;Laurent Schmalen;Thomas Zwick

{"title":"Breaking the CP Limit: Robust Long-Range OFDM Sensing via Interference Cleaning","authors":"Umut Utku Erdem;Lucas Giroto;Benedikt Geiger;Taewon Jeong;Silvio Mandelli;Christian Karle;Benjamin Nuss;Laurent Schmalen;Thomas Zwick","doi":"10.1109/TRS.2026.3704011","DOIUrl":"https://doi.org/10.1109/TRS.2026.3704011","url":null,"abstract":"In orthogonal frequency-division multiplexing (OFDM)-based radar and integrated sensing and communication systems, the sensing range is traditionally limited by the round-trip time corresponding to the cyclic prefix (CP) duration. Targets whose echoes arrive after this duration induce intersymbol interference (ISI) and associated intercarrier interference (ICI), which significantly degrade detection performance, elevate the interference-noise floor in the radar image, and reduce the useful signal power due to window mismatch. Existing methods face a tradeoff between recovering useful signal and suppressing interference, particularly in multitarget scenarios. This article proposes two frameworks to resolve this dilemma, offering a flexible tradeoff between computational cost and target detection performance. First, a signal model is derived, demonstrating that ISI- and ICI-oriented interference often dominates thermal noise in high-dynamic-range scenarios. To combat the ISI- and ICI-based interference-noise floor increase, the joint-interference cancellation with coherent compensation (JIC-CC) is proposed. This approach is an efficient evolution of the successive-interference cancellation (SIC) algorithm, utilizing high-accuracy chirp Z-transform (CZT) estimation and frequency-domain coherent compensation (FDCC) to recover weak distant targets. For scenarios requiring maximum precision, the full reconstruction-based sliding window (FR-SW) scheme is presented, which shifts the receive window to capture optimal signal energy while performing full-signal reconstruction for all detected targets. Numerical results show that both methods outperform state-of-the-art benchmarks.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1125-1140"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443139","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}

引用次数: 0

Real-Time Radar Hand Motion Recognition Using a Hybrid CNN–SVM Framework With FPGA Acceleration 基于FPGA加速的CNN-SVM混合框架实时雷达手部运动识别

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-06-05 DOI: 10.1109/TRS.2026.3700774

Taher S. Ahmed;Ahmed F. Mahmoud;Magdy Elbahnasawy;Peter F. Driessen;Ahmed Youssef

{"title":"Real-Time Radar Hand Motion Recognition Using a Hybrid CNN–SVM Framework With FPGA Acceleration","authors":"Taher S. Ahmed;Ahmed F. Mahmoud;Magdy Elbahnasawy;Peter F. Driessen;Ahmed Youssef","doi":"10.1109/TRS.2026.3700774","DOIUrl":"https://doi.org/10.1109/TRS.2026.3700774","url":null,"abstract":"Radar-based hand gesture recognition (HGR) systems often suffer from challenges related to sensor interference, environmental clutter, and limited dataset size, which complicate the training of deep learning (DL) models. This article presents an efficient radar-based HGR framework that integrates radar signal preprocessing, DL-based feature extraction, and hardware acceleration on a system-on-chip (SoC) field-programmable gate array (FPGA) platform. To investigate the impact of radar data representation on classification performance, two experimental pipelines are explored: an image-based radar representation and a continuous-valued range–time map (CV-RTM). The preprocessing stage includes clutter suppression, squared-magnitude transformation, Gaussian filtering, and normalization to enhance gesture-related radar signatures. For the image-based pipeline, binarized radar gesture images are used to train a hybrid convolutional neural network–support vector machine (CNN–SVM) classifier. The proposed CNN–SVM model achieves a CNN training accuracy of 93.3% compared to 92.2% for a CNN–LSTM–SVM architecture while reducing model parameters by 75.5% and decreasing SVM training and prediction times by 29.6% and 32.6%, respectively. For the CV-RTM pipeline, multiple DL configurations are evaluated under varying signal-to-noise ratio (SNR) conditions using a curriculum learning strategy to improve robustness in noisy radar environments. Experimental results demonstrate that the combined CNN–SVM model with curriculum learning achieves the most stable performance across SNR levels ranging from −20 to 10 dB. Finally, the optimized model is deployed on an Xilinx Kria KR260 FPGA platform using the Vitis-AI framework. The hardware implementation achieves real-time inference with significant improvements in execution speed and throughput, confirming the suitability of the proposed framework for embedded radar gesture recognition applications.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1199-1211"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148484565","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}

引用次数: 0

Labeled RFS Filter Improvements Using Measurements of Doppler Frequency 使用多普勒频率测量改进标记RFS滤波器

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-07-10 DOI: 10.1109/TRS.2026.3712106

Terell L. Dale;Tod M. Schuck;Ram M. Narayanan

引用次数: 0

Enhancing D-Band FMCW Radar Tracking for In-Air Writing Through Weakly Supervised Deep Association 弱监督深度关联增强d波段FMCW雷达空中写入跟踪

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-07-30 DOI: 10.1109/TRS.2026.3718548

Salah Abouzaid;Leander Nothelle;Nils Pohl

{"title":"Enhancing D-Band FMCW Radar Tracking for In-Air Writing Through Weakly Supervised Deep Association","authors":"Salah Abouzaid;Leander Nothelle;Nils Pohl","doi":"10.1109/TRS.2026.3718548","DOIUrl":"https://doi.org/10.1109/TRS.2026.3718548","url":null,"abstract":"Reconstructing precise character trajectories in multipath environments represents a primary challenge for high-fidelity reconstruction for radar-based in-air writing. Existing tracking methods typically rely on dominant target assumptions, often failing to account for the interference of multipath reflections. Conversely, end-to-end deep learning approaches generally lack the physical interpretability necessary for accurate geometric drawing. In this work, we propose a novel tracking framework that combines high-resolution D-band sensing with a data-driven probabilistic data association (PDA) architecture. We leverage a 56 GHz bandwidth frequency-modulated continuous-wave (FMCW) radar setup to capture fine-grained kinematic signatures, supported by a rigorous sensor placement and calibration strategy. To address multipath interference without relying on rigid parametric assumptions, we propose the Temporal Deep PDA (TD-PDA). This architecture replaces heuristic hard assignments and classical parametric clutter models with a covariance-aware causal temporal convolutional network (TCN) fused directly into the statistical covariance spread equations of an extended Kalman filter (EKF). To train the network without heuristic bias, we introduce a track quality indicator (TQI) ensemble to automatically extract high-fidelity pseudo-labels. The framework is rigorously validated via simulated ground-truth data to quantify the geometric impacts of hardware constraints, and experimentally proven on a 1000-gesture dataset using tenfold leave-one-Subject-Out (LOSO) cross-validation. The proposed TD-PDA generalizes to unseen users with an ultralow inference latency, successfully reconstructing legible trajectories even in the presence of strong multipath interference. The model demonstrates improvements over prior deep-association architectures and achieves stability comparable to a well-tuned classical PDA filter via a purely data-driven design.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1377-1394"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11631755","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148748004","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}

引用次数: 0

Assessing the Feasibility of Insect Density Quantification by Single-Polarization Weather Radar 单偏振气象雷达定量昆虫密度的可行性评估

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-09-03 DOI: 10.1109/TRS.2026.3730528

Jiayi Li;Cheng Hu;Rui Wang;Kai Cui;Zujing Yan;Xichao Dong;Dongli Wu

{"title":"Assessing the Feasibility of Insect Density Quantification by Single-Polarization Weather Radar","authors":"Jiayi Li;Cheng Hu;Rui Wang;Kai Cui;Zujing Yan;Xichao Dong;Dongli Wu","doi":"10.1109/TRS.2026.3730528","DOIUrl":"https://doi.org/10.1109/TRS.2026.3730528","url":null,"abstract":"Operational single-polarization weather radars offer the potential for large-scale, continuous monitoring of migratory insect density. However, whether single-polarization reflectivity can be quantitatively interpreted as insect density remains unresolved due to the complex and variable scattering properties of insect populations and the lack of systematic feasibility assessment. In this study, we conducted a long-term joint-observation experiment combining an operational S-band weather radar with a nearby Ku-band high-resolution fully polarimetric entomological radar providing independent local insect density measurements. Analysis across three migration seasons reveals two major constraints on reflectivity-based insect quantification. First, bird–insect co-occurrence introduces substantial positive bias in reflectivity-based retrievals. Second, even under insect-dominated conditions, strong temporal variability in effective mean radar cross section (RCS) reduces the stability of the reflectivity–density relationship, with stronger variability observed during spring and autumn than summer. A local empirical reference RCS was derived from lower error insect-dominated observations and evaluated over the complete insect-dominated dataset, showing that fixed-RCS retrieval becomes less reliable when effective mean RCS varies substantially over time. These results indicate that single-polarization insect density retrieval is constrained by biological echo composition and temporal RCS variability, highlighting the need for improved biological classification and RCS calibration approaches.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1569-1583"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033796","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}

引用次数: 0