IEEE Transactions on Radar Systems最新文献

A Virtual Aperture Extension Method for Shipborne HFSWR Based on RD-Domain Spatiotemporal Data Block Extrapolation 基于rd域时空数据块外推的舰载HFSWR虚拟孔径扩展方法

IEEE Transactions on Radar Systems Pub Date : 2025-06-02 DOI: 10.1109/TRS.2025.3575479

Youmin Qu;Xingpeng Mao;Zhibo Tang;Yiming Wang

{"title":"A Virtual Aperture Extension Method for Shipborne HFSWR Based on RD-Domain Spatiotemporal Data Block Extrapolation","authors":"Youmin Qu;Xingpeng Mao;Zhibo Tang;Yiming Wang","doi":"10.1109/TRS.2025.3575479","DOIUrl":"https://doi.org/10.1109/TRS.2025.3575479","url":null,"abstract":"To enhance the maneuverability and extend the detection range of high-frequency surface wave radar (HFSWR), shipborne systems have been developed as an alternative to shore-based platforms. However, the limited space on shipborne platforms results in a small radar array aperture, which consequently diminishes the radar’s direction-of-arrival (DOA) estimation performance. Additionally, the target echoes received by HFSWR are often accompanied by a large amount of strong clutter. Traditional extrapolation-based aperture extension methods fail because they cannot effectively distinguish between targets and clutter. Therefore, how to extend the aperture of shipborne HFSWR remains a problem to be addressed. To overcome these challenges, we improved conventional extrapolation-based aperture extension techniques by incorporating the signal processing workflow of HFSWR and proposed a novel aperture extension method for uniform linear arrays, based on range-Doppler domain spatiotemporal data block extrapolation (RDSDBE). Specifically, on the one hand, we extend the array aperture in the range-Doppler (RD) domain to address the failure of traditional aperture extension methods in the presence of strong clutter. On the other hand, we segment the target echoes in the time domain to tackle the issue of large aperture extension errors caused by the limited number of snapshots in shipborne scenarios. Through simulation and experimental data, we validated the proposed RDSDBE method and analyzed its performance.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"818-831"},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272918","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

Features and Behavioral Modeling of Ultrawideband Signals Nonlinear Scattering by Small-Sized Electronic Devices 超宽带信号在小型电子器件中的非线性散射特性及行为建模

IEEE Transactions on Radar Systems Pub Date : 2025-06-02 DOI: 10.1109/TRS.2025.3575462

Edward V. Semyonov;Maxim A. Nazarov;Kirill M. Poltorykhin;Andrey A. Berezin;Alexey V. Fateev

{"title":"Features and Behavioral Modeling of Ultrawideband Signals Nonlinear Scattering by Small-Sized Electronic Devices","authors":"Edward V. Semyonov;Maxim A. Nazarov;Kirill M. Poltorykhin;Andrey A. Berezin;Alexey V. Fateev","doi":"10.1109/TRS.2025.3575462","DOIUrl":"https://doi.org/10.1109/TRS.2025.3575462","url":null,"abstract":"It is shown that for a typical electronic gadget at test voltage pulses with a duration of ~1 ns and an electric field strength of up to ~200 V/m, the waveform of the nonlinear response from this object can be found from the test pulse by using an impulse response function, the shape of which is almost independent of the amplitude and waveform of the test pulse. The amplitude of the nonlinear object’s response is determined by both the spectral consistency between the test signal and the “test signal to nonlinear response” transfer function (signals with a higher level of high frequencies have an advantage) and by the effect of the test signal on the manifestation of the nonlinear properties of object internal circuits (signals with a higher level of low frequencies have an advantage). It has been demonstrated that the functional characterizing the influence of the test signal on the manifestation of nonlinear object’s properties is described by a quadratic dependence in the amplitude sense and is approximated by a low- pass filter in the frequency sense. By estimating the frequency properties of this filter, a measurement-based estimate of the time constantofobjects under test (about 1 ns) was obtained. On the basis of the above observations, the behavioral models of the testing objects have been synthesized. For ultrawideband pulse signals of various waveforms and amplitudes, these models give an error of no more than 17%.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"843-851"},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308288","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

Design and Field-Programmable Gate Array Realization of a Multirate Multisampling Algorithm for Improving Signal-to-Noise Ratio in Pulse Compression Radars 一种提高脉冲压缩雷达信噪比的多速率多采样算法的设计与现场可编程门阵列实现

IEEE Transactions on Radar Systems Pub Date : 2025-04-28 DOI: 10.1109/TRS.2025.3564861

Alaa G. Zahra;Ahmed Youssef;Peter F. Driessen

{"title":"Design and Field-Programmable Gate Array Realization of a Multirate Multisampling Algorithm for Improving Signal-to-Noise Ratio in Pulse Compression Radars","authors":"Alaa G. Zahra;Ahmed Youssef;Peter F. Driessen","doi":"10.1109/TRS.2025.3564861","DOIUrl":"https://doi.org/10.1109/TRS.2025.3564861","url":null,"abstract":"Due to the ongoing advancements in small unmanned systems (SUSs), the field of study on detecting targets with small radar cross section (RCS) areas is constantly expanding. Due to their widespread use in both military and civilian fields, drones are considered the most significant class of small unmanned devices, garnering significant attention. As a result, numerous methods have been developed to improve radar detection performance by mainly increasing its processing gain (PG) in order to keep up with the advancement of drone capabilities. In this article, we introduce a multirate algorithm for improving the PG of the pulsed radar to enhance its detection performance of small RCS targets. The proposed method depends on acquiring multiple samples per subpulse from the received phase-coded signal and two coherent pulse intervals (CPIs) for decision-making. The simulation results are represented to show the provided PG to the system. Moreover, the field-programmable gate array (FPGA) implementation results and the utilized resources of the suggested algorithm are shown to demonstrate the superiority of our technique compared to other conventional methods.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"756-767"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170835","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

Separability Analysis of Random FM Radar Waveforms 随机调频雷达波形的可分性分析

IEEE Transactions on Radar Systems Pub Date : 2025-04-24 DOI: 10.1109/TRS.2025.3564236

Matthew B. Heintzelman;Daniel B. Herr;Charles A. Mohr;Shannon D. Blunt;Cenk Sahin;Andrew Kordik

引用次数: 0

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

Near-Real-Time IWRAP 3D Wind Retrievals 近实时IWRAP 3D风检索

IEEE Transactions on Radar Systems Pub Date : 2025-04-23 DOI: 10.1109/TRS.2025.3563787

Joseph W. Sapp;Zorana Jelenak;Paul S. Chang;Stephen R. Guimond;James R. Carswell

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