IEEE Transactions on Radar Systems最新文献

Model-Based Knowledge-Driven Learning Approach for Enhanced High-Resolution Automotive Radar Imaging 基于模型的高分辨率汽车雷达成像知识驱动学习方法

IEEE Transactions on Radar Systems Pub Date : 2025-04-23 DOI: 10.1109/TRS.2025.3563492

Ruxin Zheng;Shunqiao Sun;Hongshan Liu;Honglei Chen;Jian Li

{"title":"Model-Based Knowledge-Driven Learning Approach for Enhanced High-Resolution Automotive Radar Imaging","authors":"Ruxin Zheng;Shunqiao Sun;Hongshan Liu;Honglei Chen;Jian Li","doi":"10.1109/TRS.2025.3563492","DOIUrl":"https://doi.org/10.1109/TRS.2025.3563492","url":null,"abstract":"Millimeter-wave (mmWave) radars are indispensable for the perception tasks of autonomous vehicles, thanks to their resilience in challenging weather and light conditions. Yet, their deployment is often limited by insufficient spatial resolution for precise semantic scene interpretation. Classical super-resolution techniques adapted from optical imaging inadequately address the distinct characteristics of radar data. In response, our study herein redefines super-resolution radar imaging as a 1-D signal super-resolution spectral estimation problem by harnessing the radar domain knowledge, introducing innovative data normalization, signal-level augmentation, and a domain-informed signal-to-noise ratio (SNR)-guided loss function. Like an image drawn with points and lines, radar imaging can be viewed as generated from points (antenna elements) and lines (frequency spectra). Our tailored deep learning (DL) network for automotive radar imaging exhibits remarkable scalability and parameter efficiency, alongside enhanced performance in terms of radar imaging quality and resolution. We further present a novel real-world dataset, pivotal for both advancing radar imaging and refining super-resolution spectral estimation techniques. Extensive testing confirms that our super-resolution angular spectral estimation network (SR-SPECNet) sets a new benchmark in producing high-resolution radar range-azimuth (RA) images, outperforming existing methods. The source code and radar dataset utilized for evaluation will be made publicly available at <uri>https://github.com/ruxinzh/SR-SPECNet</uri>","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"709-723"},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072807","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

Use of ResNet Autoencoders for Designing Phase-Quantized Sequences With Good Correlation for MIMO Radar Systems 利用ResNet自编码器设计MIMO雷达系统中相位量化序列

IEEE Transactions on Radar Systems Pub Date : 2025-04-21 DOI: 10.1109/TRS.2025.3562698

Ryota Sekiya;Hiroki Mori;Hiromi Hashimoto;Junichiro Suzuki

{"title":"Use of ResNet Autoencoders for Designing Phase-Quantized Sequences With Good Correlation for MIMO Radar Systems","authors":"Ryota Sekiya;Hiroki Mori;Hiromi Hashimoto;Junichiro Suzuki","doi":"10.1109/TRS.2025.3562698","DOIUrl":"https://doi.org/10.1109/TRS.2025.3562698","url":null,"abstract":"Multiple-input multiple-output (MIMO) radar technologies can improve radar detection capabilities and share frequencies with adjacent radar sites by transmitting nearly uncorrelated waveforms. Under certain system constraints, a set of finite-resolution digital-to-analog converters (DACs) can reduce hardware cost and power consumption. However, the waveform quantization process through DACs forces a continuous phase to lie within a discrete phase, which degrades auto- and cross-correlations. Therefore, it is usually desirable that the sequence has a finite alphabet achieving good correlation properties. Recently, uncorrelated waveform design by applying neural networks (NNs) in place of coding theory has received much attention. However, the design of phase-quantized sequences using NNs has been delicate because of differentiability with sequences modulated by discrete phase. This article proposes a framework for designing phase-quantized sequences using an NN. Numerical results show that sequences designed using the proposed framework have better correlation properties compared with those designed using existing algorithms.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"681-694"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929736","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

Image-Quality-Indicator-Based Autofocusing for High-Resolution Forward-Looking MIMO-SAR 基于图像质量指标的高分辨率前视MIMO-SAR自动对焦

IEEE Transactions on Radar Systems Pub Date : 2025-04-17 DOI: 10.1109/TRS.2025.3562000

Adnan Albaba;Marc Bauduin;S. Hamed Javadi;Eddy De Greef;André Bourdoux;Hichem Sahli

{"title":"Image-Quality-Indicator-Based Autofocusing for High-Resolution Forward-Looking MIMO-SAR","authors":"Adnan Albaba;Marc Bauduin;S. Hamed Javadi;Eddy De Greef;André Bourdoux;Hichem Sahli","doi":"10.1109/TRS.2025.3562000","DOIUrl":"https://doi.org/10.1109/TRS.2025.3562000","url":null,"abstract":"This work addresses the problem of autofocusing for forward-looking MIMO synthetic aperture radar (FL-MIMO-SAR) images. To this end, we first present and analyze the detailed geometry and signal model of the FL-MIMO-SAR autofocusing problem. Then, we propose and test a comprehensive pipeline for FL-MIMO-SAR autofocusing with automatic radar motion parameters estimation and compensation. The approach leverages a combination of three SAR image quality indicators (IQIs) to assess the performance of the autofocusing process, which is compatible with both time-domain and frequency-domain image reconstruction algorithms. Moreover, the computational complexity of the optimization problem is reduced by employing a guided backprojection (GBP) algorithm. Furthermore, we compare the three IQIs with respect to their sensitivity to different types of positioning errors. The performance of the proposed solution is quantitatively evaluated using different simulated scenarios and controlled experimental data from an anechoic chamber. Finally, we test the applicability of the proposed solution using real data from automotive scenarios. The results show that the proposed pipeline is capable of handling phase-only as well as range-cell-migration defocusing models.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"668-680"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896366","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

Unsupervised Cross-Domain Radar Target Recognition Using Multilevel Alignment 基于多水平对准的无监督跨域雷达目标识别

IEEE Transactions on Radar Systems Pub Date : 2025-04-14 DOI: 10.1109/TRS.2025.3560355

Jiawei Luan;Jinshan Ding;Yuhong Zhang

{"title":"Unsupervised Cross-Domain Radar Target Recognition Using Multilevel Alignment","authors":"Jiawei Luan;Jinshan Ding;Yuhong Zhang","doi":"10.1109/TRS.2025.3560355","DOIUrl":"https://doi.org/10.1109/TRS.2025.3560355","url":null,"abstract":"Deep learning-based automatic target recognition (ATR) for synthetic aperture radar (SAR) has made significant advancements in recent years. However, many challenges persist, particularly in cross-domain applications from simulation training to measurement recognition. Although the electromagnetic simulation can provide abundant labeled training data, the domain shift between simulation and measurement results in poor generalization performance. Current methods often aim to reduce this discrepancy without a comprehensive analysis of domain shift. We adopt a novel perspective by splitting the SAR ATR into three parts: input, feature extraction, and output to analyze the domain shift. Guided by this analysis, we propose a multilevel alignment cross-domain recognition (MACR) network designed to progressively mitigate domain shift at the input, feature, and output levels, ultimately achieving full-process domain alignment between simulation and measurement. First, the gap is bridged through mutual conversion, generating simulated-like and measured-like samples to reduce the domain shift at the input level. Subsequently, adversarial learning is employed to diminish domain shift at the feature level. Finally, cross-domain knowledge distillation and pseudolabel filtering enforce consistency regularization based on category consistency priors between unlabeled measured and simulated-like samples, reducing domain shift at the output level. Experiments conducted on the synthetic and measured paired labeled experiment (SAMPLE) and SAMPLE+ datasets demonstrate the effectiveness of the proposed MACR, achieving state-of-the-art (SOTA) performance on both datasets.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"630-644"},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875138","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

Classification of Radar Targets via Distribution Matching of Late-Time Resonance Parameters 基于后时共振参数分布匹配的雷达目标分类

IEEE Transactions on Radar Systems Pub Date : 2025-04-09 DOI: 10.1109/TRS.2025.3559394

Mihail S. Georgiev;Aaron D. Pitcher;Timothy N. Davidson

引用次数: 0

Assessment and Mitigation Approaches of 5G C-Band Interference With Aeronautical Radar Altimeter 航空雷达高度计对5G c波段干扰的评估与抑制方法

IEEE Transactions on Radar Systems Pub Date : 2025-04-02 DOI: 10.1109/TRS.2025.3557219

Aisha Elsayem;Ali Massoud;Haidy Elghamrawy;Aboelmagd Noureldin

{"title":"Assessment and Mitigation Approaches of 5G C-Band Interference With Aeronautical Radar Altimeter","authors":"Aisha Elsayem;Ali Massoud;Haidy Elghamrawy;Aboelmagd Noureldin","doi":"10.1109/TRS.2025.3557219","DOIUrl":"https://doi.org/10.1109/TRS.2025.3557219","url":null,"abstract":"The recent deployment of 5G technology in the C band has raised concerns regarding potential interference with aeronautical radar altimeters. The 5G systems in the C band operate within a frequency range of 3.7–3.98 GHz, which closely aligns with the operational frequency of radar altimeters, falling within the range of 4.2–4.4 GHz. This proximity in operational frequencies increases the possibility of interference between the two systems. In this article, we explore two primary objectives: first, to examine the potential for interference between the 5G C band and radar altimeters, and second, to develop techniques for mitigating this interference. To achieve these objectives, we assess interference in a real-world scenario, where multiple base stations (BSs) are deployed to serve an operational runway. In addition, two interference management techniques were proposed and evaluated within the assessed real-life scenario. The first involves the implementation of adaptive BS using the power control (PC) method, which aims to mitigate interference with minimal impact on coverage by adjusting the transmitting power for the BS that contributes the most to the interference model. A modification to this technique was applied to loop over the coverage areas instead of individual BSs. This technique is useful in scenarios, where BSs are implemented close to each other with overlapping coverage. Finally, a sequential quadratic programming (SQP) optimization algorithm was developed to optimize the locations of BSs, minimizing interference while maintaining coverage. This work has explored the impact of potential interference between 5G in the C band and radar altimeters and suggested practical methods to allow the coexistence of both systems, thereby ensuring aviation safety and fulfilling the telecommunication sector’s objectives.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"615-629"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875168","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

Strategies for Monitoring of Assets in Geosynchronous Orbit (GEO) Using Space-Based Sub-THz Inverse Synthetic Aperture Radar (ISAR) 基于天基亚太赫兹逆合成孔径雷达（ISAR）的地球同步轨道资产监测策略

IEEE Transactions on Radar Systems Pub Date : 2025-03-31 DOI: 10.1109/TRS.2025.3556323

Gruffudd Jones;Morgan Coe;Lily Beesley;Leah-Nani Alconcel;Marco Martorella;Marina Gashinova

{"title":"Strategies for Monitoring of Assets in Geosynchronous Orbit (GEO) Using Space-Based Sub-THz Inverse Synthetic Aperture Radar (ISAR)","authors":"Gruffudd Jones;Morgan Coe;Lily Beesley;Leah-Nani Alconcel;Marco Martorella;Marina Gashinova","doi":"10.1109/TRS.2025.3556323","DOIUrl":"https://doi.org/10.1109/TRS.2025.3556323","url":null,"abstract":"This article is concerned with the investigation and analysis of a new operational and technical capability to assess geosynchronous orbit (GEO) satellites from spaceborne platforms using extremely high-frequency radar operating at sub-THz frequencies. The concept of close monitoring and highly detailed imagery of GEO assets from all aspects, including those unattainable from the Earth, is developed based on the analysis of two proposed orbital deployment scenarios. Accounting for orbital perturbation factors during an extended period of time, the ability to build multiaspect ISAR imagery of the asset during single and multiple encounters is demonstrated, based on the mutual attitudes of the asset and the radar platform. A linearized model of the encounter geometry is presented and the approach to generate a sequence of ISAR image frames according to the geometry of the proposed scenarios is detailed. The simulation of ISAR frames at two frequency bands, centered at 75 and 300 GHz produced in a developed metaheuristic simulator, graphical electromagnetic ISAR simulator for sub-THz (GEIST), is demonstrated, to highlight the transition of scattering mechanisms and the change in visibility of particular features. Attitude-agnostic frame-to-frame image alignment and linear feature extraction using the Hough transform are then demonstrated on a sequence of simulated images.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"656-667"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929683","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

Low-Complexity Multitarget Detection and Localization Method for Distributed MIMO Radar 分布式多输入多输出雷达的低复杂度多目标探测和定位方法

IEEE Transactions on Radar Systems Pub Date : 2025-03-24 DOI: 10.1109/TRS.2025.3554198

Ruilin Chen;Shisheng Guo;Jiahui Chen;Xingyu Gu;Guolong Cui;Lingjiang Kong;Weijian Liu

{"title":"Low-Complexity Multitarget Detection and Localization Method for Distributed MIMO Radar","authors":"Ruilin Chen;Shisheng Guo;Jiahui Chen;Xingyu Gu;Guolong Cui;Lingjiang Kong;Weijian Liu","doi":"10.1109/TRS.2025.3554198","DOIUrl":"https://doi.org/10.1109/TRS.2025.3554198","url":null,"abstract":"Direct position determination (DPD) for multiple targets in distributed multiple-input multiple-output (MIMO) radar has been a challenging problem. This article proposed a low-complexity multitarget detection and localization method for distributed MIMO radar. To address the problem of exponential expansion of the state space caused by high-dimensional detection in traditional DPD, a low-dimensional detector is proposed. Specifically, we divide the radar-sensed scene into discrete 2-D grid cells and derive the maximum likelihood estimation (MLE) function as well as the generalized likelihood ratio test (GLRT) detector in the 2-D scene. In addition, the probability of a false alarm (PFA) for the derived GLRT detector has an analytic solution, ensuring each grid cell maintains a constant PFA. Since the proposed detector introduces a large number of false targets, we further propose the clean with protected cells (CPCs) algorithm to remove false targets and localize real targets. This method generates protection points based on the relationship between the real targets and the radar channels, achieving high-accuracy localization with low computational complexity, even in scenes with inseparable targets. Finally, both numerical simulations and experimental data demonstrate the effectiveness of the proposed method. Simulation results show that the proposed method achieves the best detection performance compared to state-of-the-art methods, with an average processing time of only 565.7 ms, meeting the requirements for real-time target detection and localization.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"599-614"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821236","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 Low-Cost and Compact Software-Defined UWB Transmitter for Radar Utilizing a Nonlinear Transmission Line 一种基于非线性传输线的低成本、紧凑型软件定义超宽带雷达发射机

IEEE Transactions on Radar Systems Pub Date : 2025-03-24 DOI: 10.1109/TRS.2025.3554135

Tyler Kelley;Stephen Pancrazio;Samuel Wagner;Ababil Hossain;Nhat Tran;Anh-Vu Pham

引用次数: 0

Adaptive Frame-Rate Partitioned Video SAR 自适应帧率分割视频SAR

IEEE Transactions on Radar Systems Pub Date : 2025-03-20 DOI: 10.1109/TRS.2025.3553116

Zhengyang Sun;Liwu Wen;Jinshan Ding

{"title":"Adaptive Frame-Rate Partitioned Video SAR","authors":"Zhengyang Sun;Liwu Wen;Jinshan Ding","doi":"10.1109/TRS.2025.3553116","DOIUrl":"https://doi.org/10.1109/TRS.2025.3553116","url":null,"abstract":"Video synthetic aperture radar (ViSAR) is a promising technology for the surveillance of ground-moving targets. Traditionally, ViSAR imaging and moving target tracking are performed sequentially, where high-frame-rate imaging is applied to the entire SAR scene. However, this approach generates redundant information that is often unnecessary for ViSAR applications. We propose a partitioned adaptive frame-rate (PAFR) ViSAR processing strategy, which adaptively partitions the SAR scene, applying high-frame-rate imaging to potential target regions and low frame rate to large static areas. An integrated imaging and tracking algorithm that synthesizes back-projection (BP) and track-before-detect (TBD) techniques has been derived for efficient bidirectional information exchange. BP imaging provides high-resolution measurements to refine tracking parameters, while TBD tracking offers predictive data to guide the adaptive partitioning of the imaging area. Additionally, we enhance the traditional dynamic programming-based TBD (DP-TBD) algorithm by incorporating the morphological features of target shadows, allowing for more accurate corrections and refinements of predicted states. This enhancement significantly improves both tracking accuracy and speed. The experimental results from airborne radar data have proven the capability of the proposed algorithm to achieve both efficient PAFR imaging and fast target tracking simultaneously, which paves the way for more potential applications in ViSAR.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"576-590"},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792926","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