IEEE Transactions on Radar Systems最新文献_第2页

Adaptive Focused Observations on the Advanced Technology Demonstrator Phased-Array Radar 先进技术验证型相控阵雷达的自适应聚焦观测

IEEE Transactions on Radar Systems Pub Date : 2026-01-21 DOI: 10.1109/TRS.2026.3656643

Sebastián M. Torres;Christopher D. Curtis;Robert J. Estes;Stephen B. Gregg

{"title":"Adaptive Focused Observations on the Advanced Technology Demonstrator Phased-Array Radar","authors":"Sebastián M. Torres;Christopher D. Curtis;Robert J. Estes;Stephen B. Gregg","doi":"10.1109/TRS.2026.3656643","DOIUrl":"https://doi.org/10.1109/TRS.2026.3656643","url":null,"abstract":"The advanced technology demonstrator (ATD) is a full-scale, S-band, dual-polarization phased-array radar (PAR) developed as a proof-of-concept to evaluate the capabilities of electronically scanned radars for weather observation. Recent upgrades to the ATD have introduced a closed-loop adaptive scanning framework and an implementation of the adaptive digital signal-processing algorithm for PAR timely scans (ADAPTSs) to enable adaptive focused observations. ADAPTS leverages the radar’s beam agility to dynamically concentrate measurements in regions with significant weather returns, reducing the total scan time while preserving data quality and meaningful volumetric coverage. The algorithm selectively enables (disables) beams with (without) significant weather echoes, enables beams in a neighborhood of beams with significant weather echoes to account for storm advection and growth, and periodically reactivates disabled beams to detect new storm development. Performance evaluations using archived and real-time data demonstrate substantial reductions in scan time, rapid detection of storm initiation, and high-fidelity coverage of active weather regions. These results highlight the potential of adaptive scanning strategies for improving weather observations using PAR systems.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"463-472"},"PeriodicalIF":0.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175606","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

TD-AMRKNet-Based Radar Image Processing Framework for Sea Clutter Suppression 基于td - amrknet的海杂波抑制雷达图像处理框架

IEEE Transactions on Radar Systems Pub Date : 2026-01-20 DOI: 10.1109/TRS.2026.3656433

Xiaolin Du;Di Ma;Xiaolong Chen;Guolong Cui;Jibin Zheng

{"title":"TD-AMRKNet-Based Radar Image Processing Framework for Sea Clutter Suppression","authors":"Xiaolin Du;Di Ma;Xiaolong Chen;Guolong Cui;Jibin Zheng","doi":"10.1109/TRS.2026.3656433","DOIUrl":"https://doi.org/10.1109/TRS.2026.3656433","url":null,"abstract":"Sea clutter significantly impacts the radar detection of maritime targets. Existing sea clutter suppression methods often face challenges in complex, dynamic marine environments, and their generalization capabilities may be limited. This article proposes a network architecture named triplet diffusion attention multiscale Res-KAN Net (TD-AMRKNet), based on a diffusion model and triplet attention. By introducing lightweight multiscale generalized spatial convolutions (multiscale-GSConvs) and several small model networks, TD-AMRKNet effectively reduces model parameters, making it a compact and efficient network. The AMRK module, designed with a gating mechanism, captures long-range dependencies in images. It also integrates multisource knowledge through cross-resolution image fusion, thereby enhancing semantic understanding and improving the representation of details and local features. TD-AMRKNet effectively suppresses sea clutter across different radar data types, including time–frequency spectrograms from staring radar and PPI images from scanning radar. Experimental results show that the model contains only 3.46-M parameters and requires approximately 0.0305 s for overall average (OA) processing. It achieves competitive performance on six real-world sea clutter datasets, with clutter suppression effectiveness evaluated using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), P-S, and clutter suppression ratio (CSR) metrics.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"387-406"},"PeriodicalIF":0.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082077","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

Time and Phase Synchronization of a Broadband Multistatic Imaging Radar Network Using Non-Cooperative Signals 基于非合作信号的宽带多静态成像雷达网络时相同步

IEEE Transactions on Radar Systems Pub Date : 2026-01-15 DOI: 10.1109/TRS.2026.3654640

Fabian Hochberg;Matthias Jirousek;Simon Anger;Markus Peichl;Thomas Zwick

{"title":"Time and Phase Synchronization of a Broadband Multistatic Imaging Radar Network Using Non-Cooperative Signals","authors":"Fabian Hochberg;Matthias Jirousek;Simon Anger;Markus Peichl;Thomas Zwick","doi":"10.1109/TRS.2026.3654640","DOIUrl":"https://doi.org/10.1109/TRS.2026.3654640","url":null,"abstract":"High-resolution multistatic imaging radar systems pose significant challenges to the employed synchronization schemes, as such radar networks need to operate coherently. Especially high-resolution systems operating at a high center frequency push the required synchronization requirements into the single-digit picosecond regime. Within the project Imaging of Satellites in Space—Next Generation (IoSiS-NG), the task at hand is further challenged by the use of far baselines, where commonly seen approaches fail, as no line-of-sight (LOS) free-space propagation or wired method can be employed. In this article, a robust synchronization method is presented that elevates well-established global navigation satellite systems (GNSSs)-based methods by about three orders of magnitude through the coordinated reception of non-cooperative (NC) signals at all participating nodes. Exploiting the identical signal payload at all stations, the timing and phase differences of the nodes can be tracked and corrected in the post-processing stage. Here, we demonstrate our newly developed algorithm, simulative studies and real-world experiments using satellite broadcast television (TV) signals as NC signals to synchronize a high-resolution imaging radar achieving a timing standard deviation of less than 1.8 ps and a phase coherence for the X-band radar of less than 2° allowing interferometric or tomographic imaging principles to be used.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"373-386"},"PeriodicalIF":0.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082144","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

Collaborative Learning of Scattering and Deep Features for SAR Target Recognition With Noisy Labels 带噪声标签SAR目标识别中散射与深度特征的协同学习

IEEE Transactions on Radar Systems Pub Date : 2026-01-15 DOI: 10.1109/TRS.2026.3654779

Yimin Fu;Zhunga Liu;Dongxiu Guo;Longfei Wang

{"title":"Collaborative Learning of Scattering and Deep Features for SAR Target Recognition With Noisy Labels","authors":"Yimin Fu;Zhunga Liu;Dongxiu Guo;Longfei Wang","doi":"10.1109/TRS.2026.3654779","DOIUrl":"https://doi.org/10.1109/TRS.2026.3654779","url":null,"abstract":"The acquisition of high-quality labeled synthetic aperture radar (SAR) data is challenging due to the demanding requirement for expert knowledge. Consequently, the presence of unreliable noisy labels is unavoidable, which results in performance degradation of SAR automatic target recognition (ATR). Existing research on learning with noisy labels mainly focuses on image data. However, the nonintuitive visual characteristics of SAR data are insufficient to achieve noise-robust learning. To address this problem, we propose collaborative learning of scattering and deep features (CLSDFs) for SAR ATR with noisy labels. Specifically, a multimodel feature fusion framework is designed to integrate scattering and deep features. The attributed scattering centers (ASCs) are treated as dynamic graph structure data, and the extracted physical characteristics effectively enrich the representation of deep image features. Then, the samples with clean and noisy labels are divided by modeling the loss distribution with multiple class-wise Gaussian mixture models (GMMs). Afterward, the semi-supervised learning of two divergent branches is conducted based on the data divided by each other. Moreover, a joint distribution alignment (JDA) strategy is introduced to enhance the reliability of coguessed labels. Extensive experiments have been done on the moving and stationary target acquisition and recognition (MSTAR) and SAR-ACD datasets, and the results show that the proposed method can achieve state-of-the-art performance under different operating conditions with various label noises. The code is released at <uri>https://github.com/fuyimin96/CLSDF</uri>","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"359-372"},"PeriodicalIF":0.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082106","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

2025 Index IEEE Transactions on Radar Systems 雷达系统学报

IEEE Transactions on Radar Systems Pub Date : 2026-01-14 DOI: 10.1109/TRS.2026.3654820

引用次数: 0

Unsupervised Federated Learning With Harmonized Foundation Models for FMCW Radar-Based Hand Gesture Recognition 基于FMCW雷达手势识别的协调基础模型无监督联邦学习

IEEE Transactions on Radar Systems Pub Date : 2026-01-14 DOI: 10.1109/TRS.2026.3654128

Tobias Sukianto;Matthias Wagner;Maximilian Strobel;Sarah Seifi;Cecilia Carbonelli;Mario Huemer

{"title":"Unsupervised Federated Learning With Harmonized Foundation Models for FMCW Radar-Based Hand Gesture Recognition","authors":"Tobias Sukianto;Matthias Wagner;Maximilian Strobel;Sarah Seifi;Cecilia Carbonelli;Mario Huemer","doi":"10.1109/TRS.2026.3654128","DOIUrl":"https://doi.org/10.1109/TRS.2026.3654128","url":null,"abstract":"Frequency-modulated continuous wave (FMCW) radar-based hand gesture recognition (HGR) systems face deployment challenges due to variations in radar hardware, antenna layouts, and gesture classes, which cause distributional shifts across devices. These shifts limit the effectiveness of transfer learning (TL), which, while helpful for reusing knowledge, struggles with significant hardware and configuration changes and often requires substantial labeled data from the target domain. We present a radar-specific adaptation framework that enables cross-device gesture recognition with minimal labeled data. A key component is the harmonization module (HM), which performs signal-level transformations to align the range and Doppler dimensions of radar data across differing configurations. In parallel, radar-specific data augmentation techniques simulate missing antenna channels and gesture variability to improve pretraining robustness. A transformer-based foundation model is pretrained on harmonized and augmented data from a source radar and then fine-tuned using a small number of labeled samples from the target configuration. The adapted model is distilled into a lightweight architecture and deployed to clients sharing the same radar setup using an unsupervised federated learning (FL) pipeline. This enables on-device model refinement using only unlabeled data. Experiments on public datasets from Infineon and Texas Instruments radars show that our method achieves over 94% accuracy with just 20 labeled samples per class, outperforming baselines by more than 10%, and converging with four times fewer communication rounds during FL.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"443-462"},"PeriodicalIF":0.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175801","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

Enhancement of Forward-Looking Imaging Based on Sparse MIMO Radar via Motion-Based Multiframe Inversion 基于运动的多帧反演增强稀疏MIMO雷达前视成像

IEEE Transactions on Radar Systems Pub Date : 2026-01-12 DOI: 10.1109/TRS.2026.3652673

Qiqiang Zou;Hongda Guan;Chang Chen;Yingbin Chen;Yixiong Zhang;Caipin Li;Sailong Yang

{"title":"Enhancement of Forward-Looking Imaging Based on Sparse MIMO Radar via Motion-Based Multiframe Inversion","authors":"Qiqiang Zou;Hongda Guan;Chang Chen;Yingbin Chen;Yixiong Zhang;Caipin Li;Sailong Yang","doi":"10.1109/TRS.2026.3652673","DOIUrl":"https://doi.org/10.1109/TRS.2026.3652673","url":null,"abstract":"Radar forward-looking (FL) imaging plays a crucial role in achieving high-resolution environmental perception. When a sparse array configuration is used, the imaging resolution can be improved. However, it is inevitably accompanied by severe grating lobes. Although multiframe observations can be used to mitigate the influence of grating lobes, they often introduce additional artifacts. Existing approaches remain inadequate in artifact suppression and unavoidably attenuate weak targets. To address these issues, we propose an adaptive weight strategy that leverages the statistical characteristics of multiframe data to suppress artifacts and improve the fidelity of target amplitude reconstruction. First, the energy of multichannel signals is computed for each frame, forming a multiframe energy sequence. Second, an adaptive weight is calculated based on the mean and variance of this sequence. Finally, an exponential factor of the weight is applied to enhance the spectral reconstruction by enlarging the discrimination between true targets and artifacts. The effectiveness of the proposed method is validated through simulation and measurement experiments, demonstrating its advantages over existing methods.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"353-358"},"PeriodicalIF":0.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175797","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

Joint Moving Targets and Stationary Scene Imaging for Multichannel Forward-Looking SAR 多通道前视SAR运动目标与静止场景联合成像

IEEE Transactions on Radar Systems Pub Date : 2026-01-12 DOI: 10.1109/TRS.2026.3651703

Rui Chen;Wenchao Li;Jianyu Yang;Kun Zhang;Bowen Cheng;Zhongyu Li;Junjie Wu

{"title":"Joint Moving Targets and Stationary Scene Imaging for Multichannel Forward-Looking SAR","authors":"Rui Chen;Wenchao Li;Jianyu Yang;Kun Zhang;Bowen Cheng;Zhongyu Li;Junjie Wu","doi":"10.1109/TRS.2026.3651703","DOIUrl":"https://doi.org/10.1109/TRS.2026.3651703","url":null,"abstract":"By simultaneously receiving echoes from multiple channels in azimuth, multichannel radars can achieve forward-looking imaging of stationary scenes. However, the moving targets would be submerged in the stationary scene due to the azimuth offset and defocusing phenomena. In this article, a joint imaging scheme for moving targets and a stationary scene in multichannel forward-looking synthetic aperture radar (SAR) is proposed. First, the echo data is processed using the spatial-domain BP to achieve azimuth focusing of each snapshot and eliminate the space-variant Doppler centroid introduced by the platform motion, and then coherent accumulation is performed on the results of each snapshot to obtain the imaging result of a stationary scene. Second, based on the imaging results of each snapshot, a high-pass filter is used to suppress the spectrum of the stationary scene, and then the Doppler centroid and chirp rate introduced by the moving target are estimated, respectively. Then, by compensating the echo phase snapshot by snapshot with the estimated parameters and performing coherent accumulation, the refocusing of moving targets can be achieved. At last, by combining the imaging results of a stationary scene with the refocusing results of moving targets, the forward-looking SAR imaging result containing moving target indications can be obtained. The results of simulation experiments and measured data have demonstrated the effectiveness of the proposed scheme.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"338-352"},"PeriodicalIF":0.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026423","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

Photonics-Based Superresolution Radar Imaging With Space-Time-Frequency Coincidence Processing 基于空时频重合处理的光子学超分辨率雷达成像

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-06-03 DOI: 10.1109/TRS.2026.3699825

Xiaoyue Yu;Qing Xiong;Fangzheng Zhang;Xin Yan;Yansen He;Gong Zhang;Xiangchuan Wang;Zhenyu Xu;Biao Xu;Shilong Pan

{"title":"Photonics-Based Superresolution Radar Imaging With Space-Time-Frequency Coincidence Processing","authors":"Xiaoyue Yu;Qing Xiong;Fangzheng Zhang;Xin Yan;Yansen He;Gong Zhang;Xiangchuan Wang;Zhenyu Xu;Biao Xu;Shilong Pan","doi":"10.1109/TRS.2026.3699825","DOIUrl":"https://doi.org/10.1109/TRS.2026.3699825","url":null,"abstract":"A photonics-based, superresolution radar imaging system using space-time-frequency coincidence processing is proposed and experimentally demonstrated. In the transmitter, a broadband, frequency-hopping (FH) signal is generated by an optically injected semiconductor laser based on the P1 oscillation dynamics. The generated signal is switched and transmitted through a uniform linear antenna array (ULA) by means of time division multiplexing (TDM). Therein, a space-time-frequency, 3-D stochastic radiation field is constructed, which is manifested as wavefront modulation. In the receiver, the radar echoes are received and frequency downconverted to baseband through IQ mixing. Thanks to the broadband operation capability of the microwave photonic system, high range resolution can be easily achieved. To overcome the azimuth resolution limitation determined by the array aperture size, a kernel function-based sparse reconstruction algorithm is proposed, which enables superresolution coincidence imaging in the azimuth direction. In the experiments, a photonics-based <inline-formula> <tex-math>$4times 1$ </tex-math></inline-formula> multiple-input single-output (MISO) radar with a bandwidth of 8 GHz (10–18 GHz) is established, in which the transmitted signal is a 32-level FH signal. Combined with the proposed coincidence imaging algorithm, superresolution radar imaging is realized with the range-azimuth imaging resolution reaching <inline-formula> <tex-math>$2.3times 0.8$ </tex-math></inline-formula> cm, which surpasses that of the traditional MISO radar by 20 times in azimuth resolution. In addition, the imaging experiments of complex targets with N, V, and A shapes and an aircraft model with a continuous structure are demonstrated to imitate real scenes, which verify the feasibility of the proposed photonics-based radar system in high-resolution forward-looking imaging.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1016-1024"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236229","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

Addressing Boundary Artifacts When Using Radar Imaging on Phased Array Weather Radars 相控阵气象雷达成像时边界伪影的寻址

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-03-06 DOI: 10.1109/TRS.2026.3691013

Christopher D. Curtis;Sebastián M. Torres;Rakshith Jagannath

{"title":"Addressing Boundary Artifacts When Using Radar Imaging on Phased Array Weather Radars","authors":"Christopher D. Curtis;Sebastián M. Torres;Rakshith Jagannath","doi":"10.1109/TRS.2026.3691013","DOIUrl":"https://doi.org/10.1109/TRS.2026.3691013","url":null,"abstract":"Radar imaging is a method for using spoiled transmit beams with multiple receive beams to speed up data collection with phased array radars (PARs). It introduces tradeoffs in sensitivity and angular resolution and can also lead to data artifacts along beam cluster boundaries (boundaries between radar data obtained with adjacent spoiled transmit beams). We show that these data artifacts are due to asymmetric edge beams that occur when the spoiled transmit beam is too narrow for a given set of receive beams. We use both simulations and derivations to quantify the bias in reflectivity gradients along beam cluster boundaries. The derivations are also used to develop design criteria to ensure that the spoiled transmit beam is wide enough to produce symmetric edge beams that mitigate data artifacts. We use data collected with the advanced technology demonstrator (ATD) to show the presence of data artifacts when the spoiled transmit beam is too narrow and the absence of artifacts when the spoiled transmit beam is wide enough to produce symmetric edge beams. We also use a quantitative evaluation of the reflectivity gradient biases in collected weather data to validate both our simulations and derivations.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"930-942"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003458","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