IEEE Transactions on Radar Systems最新文献_第6页

TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection TransRAD：用于增强雷达目标检测的保留视觉转换器

IEEE Transactions on Radar Systems Pub Date : 2025-02-03 DOI: 10.1109/TRS.2025.3537604

Lei Cheng;Siyang Cao

{"title":"TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection","authors":"Lei Cheng;Siyang Cao","doi":"10.1109/TRS.2025.3537604","DOIUrl":"https://doi.org/10.1109/TRS.2025.3537604","url":null,"abstract":"Despite significant advancements in environment perception capabilities for autonomous driving and intelligent robotics, cameras and LiDARs remain notoriously unreliable in low-light conditions and adverse weather, which limits their effectiveness. Radar serves as a reliable and low-cost sensor that can effectively complement these limitations. However, radar-based object detection has been underexplored due to the inherent weaknesses of radar data, such as low resolution, high noise, and lack of visual information. In this article, we present TransRAD, a novel 3-D radar object detection model designed to address these challenges by leveraging the retentive vision transformer (RMT) to more effectively learn features from information-dense radar range-Azimuth–Doppler (RAD) data. Our approach leverages the retentive Manhattan self-attention (MaSA) mechanism provided by RMT to incorporate explicit spatial priors, thereby enabling more accurate alignment with the spatial saliency characteristics of radar targets in RAD data and achieving precise 3-D radar detection across RAD dimensions. Furthermore, we propose location-aware nonmaximum suppression (LA-NMS) to effectively mitigate the common issue of duplicate bounding boxes in deep radar object detection. The experimental results demonstrate that TransRAD outperforms state-of-the-art (SOTA) methods in both 2-D and 3-D radar detection tasks, achieving higher accuracy, faster inference speed, and reduced computational complexity. Code is available at <uri>https://github.com/radar-lab/TransRAD</uri>.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"303-317"},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422870","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

A Novel High-Resolution Imaging Method Based on Nonlinear Wavefront Modulation 一种基于非线性波前调制的高分辨率成像方法

IEEE Transactions on Radar Systems Pub Date : 2025-01-29 DOI: 10.1109/TRS.2025.3535913

Mengyang Shi;Yesheng Gao;Jiahui Ma;Wenxuan Shi;Bin Yuan;Xingzhao Liu

引用次数: 0

Instantaneous Single-Station Location and Velocity Estimation of Airborne Noncooperative Emitter Using Clutter Angle-Doppler Frequencies 基于杂波角-多普勒频率的机载非合作辐射源瞬时单站定位与速度估计

IEEE Transactions on Radar Systems Pub Date : 2025-01-27 DOI: 10.1109/TRS.2025.3534805

Yuxuan Zhang;Jianxin Wu;Lei Zhang;Mengya Li

{"title":"Instantaneous Single-Station Location and Velocity Estimation of Airborne Noncooperative Emitter Using Clutter Angle-Doppler Frequencies","authors":"Yuxuan Zhang;Jianxin Wu;Lei Zhang;Mengya Li","doi":"10.1109/TRS.2025.3534805","DOIUrl":"https://doi.org/10.1109/TRS.2025.3534805","url":null,"abstract":"Due to the unknown location of the noncooperative airborne emitter, their application as external radiation for passive radar is limited. Therefore, its location and velocity estimation is a significant problem. This article first proposes an airborne emitter location and velocity estimation method in airborne passive radar using angle-Doppler frequencies on the clutter spectrum peaks (SPs). First, the frequencies on clutter SPs in each range bin are estimated by the minimum variance distortionless response (MVDR) of clutter with sub-aperture smoothing (SASM) techniques. Second, a parametric approach is proposed to formulate the relationship between the frequencies and parameters of interest. Finally, an intermediate unknown is introduced to simplify the 6-D parameter optimization problem into a 1-D search. The advantages: The proposed method utilizes clutter information to enhance observability of single-station in a short period, and robustly estimate the location and velocity with a closed-form solution. Cramer-Rao lower bound (CRLB) is derived for theoretical analysis. Simulation experiments validate the effectiveness and advantages of the proposed method.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"392-405"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521494","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

Space-Domain Awareness Using Over-the-Horizon Radar 利用超视距雷达进行空间域感知

IEEE Transactions on Radar Systems Pub Date : 2025-01-27 DOI: 10.1109/TRS.2025.3534521

Simon Henault;Kyra Czarnowske;Yahia M. M. Antar

引用次数: 0

Range–Doppler Resolution Enhancement of Ground-Based Radar by Data Extrapolation Technique 利用数据外推技术提高地基雷达距离-多普勒分辨率

IEEE Transactions on Radar Systems Pub Date : 2025-01-23 DOI: 10.1109/TRS.2025.3533496

Liang Zhang;Qinglei Du;Weijian Liu;Hui Chen;Yongliang Wang

{"title":"Range–Doppler Resolution Enhancement of Ground-Based Radar by Data Extrapolation Technique","authors":"Liang Zhang;Qinglei Du;Weijian Liu;Hui Chen;Yongliang Wang","doi":"10.1109/TRS.2025.3533496","DOIUrl":"https://doi.org/10.1109/TRS.2025.3533496","url":null,"abstract":"A method based on data extrapolation is proposed to enhance the range-Doppler (RD) resolution of ground-based radar. The method employs both the bandwidth extrapolation (BWE), a well-known range super-resolution technique, and the time width extrapolation (TWE), an interesting extension of BWE from the fast-time frequency to slow time, to construct the radar returns with larger bandwidth and more pulses. This method consists of two key parts: first performing TWE on the echoes after pulse compression and then performing BWE on the integrated echoes, which can bring better performance because the gain in radar signal processing is fully utilized. Not only this, an improved data extrapolation technique is designed and applied to the proposed method. The improvement is the generalization of the existing linear prediction-based extrapolation method and can ensure that radar resolution is further enhanced if a parameter of the suppression factor is set reasonably. The experiments based on the simulated data and the observations of vehicles on highway by an S-band ground-based radar illustrate that radar resolution can be improved by a factor of at least 3 with almost undistorted RD images and clutter narrowed in Doppler using a conservative suppression factor setting of <inline-formula> <tex-math>$0.5sim 1$ </tex-math></inline-formula>.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"290-302"},"PeriodicalIF":0.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360948","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

OFDM-Based Remote Sensing Using the 4-D Modified Matrix Pencil Method 基于ofdm的四维改进矩阵笔法遥感

IEEE Transactions on Radar Systems Pub Date : 2025-01-22 DOI: 10.1109/TRS.2025.3532831

Samuel P. Lavery;Tharmalingam Ratnarajah

引用次数: 0

Polarization-Agile Jamming Suppression for Dual-Polarized Digital Array Radars 双极化数字阵列雷达极化敏捷干扰抑制

IEEE Transactions on Radar Systems Pub Date : 2025-01-16 DOI: 10.1109/TRS.2025.3530404

Zhigang Wang;Jin He;Ting Shu;Ning Zhang;Xiang Lu;Junfeng Wang;Trieu-Kien Truong

{"title":"Polarization-Agile Jamming Suppression for Dual-Polarized Digital Array Radars","authors":"Zhigang Wang;Jin He;Ting Shu;Ning Zhang;Xiang Lu;Junfeng Wang;Trieu-Kien Truong","doi":"10.1109/TRS.2025.3530404","DOIUrl":"https://doi.org/10.1109/TRS.2025.3530404","url":null,"abstract":"In the realm of modern radar electronic warfare, hostile jamming signals with time-variant polarization states pose a significant challenge to the performance of host radars. This article presents a signal-processing scheme specifically designed to suppress polarization-agile jamming signals in dual-polarized digital array radars (DARs). By innovatively modeling the polarization-agile jamming signal as two orthogonal linearly polarized signals sharing the same elevation-azimuth angle, a direction-cosine estimation and association algorithm tailored for such signals is derived. Furthermore, a spatial covariance matrix reconstruction (CMR) method that uniquely extracts the time-varying polarization parameters of each jamming signal is developed. Building upon this, a spatial-polarization CMR method is devised to effectively suppress all polarization-agile jamming signals. The key innovation lies in achieving adaptive polarization matching during the cancellation process, which sets this scheme apart from conventional radar signal-processing approaches. Simulation results underscore the superiority of the proposed scheme, demonstrating significant performance enhancements over commonly used methodologies.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"247-259"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105941","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

Identification and High-Accuracy Range Estimation With Doppler Tags in Radar Applications 多普勒标签在雷达中的识别与高精度距离估计

IEEE Transactions on Radar Systems Pub Date : 2025-01-16 DOI: 10.1109/TRS.2025.3530560

Theresa Antes;Paul Schubert;Thomas Zwick;Benjamin Nuss

引用次数: 0

Flexible Compression for Efficient Information Sharing in a Network of Radio Frequency Sensors 射频传感器网络中有效信息共享的柔性压缩

IEEE Transactions on Radar Systems Pub Date : 2025-01-14 DOI: 10.1109/TRS.2025.3529760

Fraser K. Coutts;John Thompson;Bernard Mulgrew

{"title":"Flexible Compression for Efficient Information Sharing in a Network of Radio Frequency Sensors","authors":"Fraser K. Coutts;John Thompson;Bernard Mulgrew","doi":"10.1109/TRS.2025.3529760","DOIUrl":"https://doi.org/10.1109/TRS.2025.3529760","url":null,"abstract":"The efficient extraction of useful information from radio frequency (RF) sensors is one important application for artificial intelligence (AI) and machine learning (ML) approaches. In particular, there is a desire to maximize efficiency when sharing positioning, navigation, and timing (PNT) information captured by distributed networks of low size, weight, power, and cost (SWAP-C) RF sensors when operating in congested or contested electromagnetic environments (EMEs). By implementing effective PNT information-sharing strategies, these networks can more easily position the sensors or characterize targets of interest. In this work, we propose a novel ML-inspired compression design framework that improves efficiency when sharing PNT information in a network of sensors receiving radar waveforms. In addition, through novel learning procedures, the network can adapt to unforeseen EMEs such that network efficiency can be maintained in the presence of unforeseen RF waveforms and sensor surroundings. We show that our intelligent, model-driven, ML-inspired data reduction strategies can outperform alternative strategies that do not best-utilize the information content of waveforms in the EME. In addition, we demonstrate the ability of our strategies to adapt to changing mission goals by balancing different types of PNT information and learning from developing EMEs.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"332-348"},"PeriodicalIF":0.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422868","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

Stepped-Frequency PMCW Waveforms for Automotive Radar Applications 用于汽车雷达的步进频率PMCW波形

IEEE Transactions on Radar Systems Pub Date : 2025-01-13 DOI: 10.1109/TRS.2025.3528773

Moritz Kahlert;Tai Fei;Claas Tebruegge;Markus Gardill

引用次数: 0