IEEE Transactions on Radar Systems最新文献

ClassiGAN: Joint Image Reconstruction and Classification in Computational Microwave Imaging

IEEE Transactions on Radar Systems Pub Date : 2025-02-19 DOI: 10.1109/TRS.2025.3543722

Jiaming Zhang;Guillermo Álvarez-Narciandi;María García-Fernández;Rahul Sharma;Jie Zhang;Philipp del Hougne;Muhammad Ali Babar Abbasi;Okan Yurduseven

{"title":"ClassiGAN: Joint Image Reconstruction and Classification in Computational Microwave Imaging","authors":"Jiaming Zhang;Guillermo Álvarez-Narciandi;María García-Fernández;Rahul Sharma;Jie Zhang;Philipp del Hougne;Muhammad Ali Babar Abbasi;Okan Yurduseven","doi":"10.1109/TRS.2025.3543722","DOIUrl":"https://doi.org/10.1109/TRS.2025.3543722","url":null,"abstract":"Computational imaging (CI)-based systems have emerged as a viable alternative to address the challenges of high hardware complexity and slow data acquisition speed associated with conventional microwave imaging. However, CI-based systems are limited by a substantial computational burden during the scene reconstruction process. In particular, image reconstruction and target classification problems for CI systems are computationally complex tasks. To tackle this challenge, a generative deep learning model named ClassiGAN is proposed to jointly solve the image reconstruction and target classification tasks by only using the backscattered measured signals as input. In particular, an adaptive loss function is employed to effectively integrate the respective loss functions for the two tasks, thereby enhancing training efficiency. This adaptive loss function dynamically adjusts the weights of the losses associated with each task, facilitating a more effective integration of the differing loss functions. Notably, ClassiGAN significantly reduces the run time for image reconstruction tasks compared to conventional CI methods. Compared to other state-of-the-art methods, ClassiGAN not only achieves lower average normalized mean squared error (NMSE) and higher structural similarity (SSIM) but also provides a higher accuracy in recognizing imaging targets. Extensive experimental tests further validate ClassiGAN’s capability to simultaneously reconstruct and recognize the imaging target within practical settings. Hence, this shows that ClassiGAN can enhance the overall efficiency of CI-based systems at microwave frequencies by addressing challenges related to computational load during run time.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"441-452"},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553115","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

Dual-Channel Joint SAR-Interferometry via Superresolution Spectral Estimation

IEEE Transactions on Radar Systems Pub Date : 2025-02-17 DOI: 10.1109/TRS.2025.3542699

Alex Batts;Brian Rigling

{"title":"Dual-Channel Joint SAR-Interferometry via Superresolution Spectral Estimation","authors":"Alex Batts;Brian Rigling","doi":"10.1109/TRS.2025.3542699","DOIUrl":"https://doi.org/10.1109/TRS.2025.3542699","url":null,"abstract":"Interferometric synthetic aperture radar (SAR) utilizes the phase difference between two images formed from separate channels to extract information from the imaged scene. Dual-channel systems provide a compromise between multipass and multichannel setups in that greater coherence between the channels is achieved while still being physically realizable. However, dual-channel systems suffer from less stability in phase estimates due to the inability to undergo sufficient averaging to reduce thermal noise. Spectral estimation techniques have the ability to reduce these effects and provide stable, accurate intensity and phase estimates. This article presents a novel extension of a previously developed technique for height estimation to the Amplitude and Phase EStimation (APES) filter, and develops a novel technique using linear prediction filters. In addition, the three techniques are extended to along-track interferometric phase stabilization for moving target indication (MTI). Quantitative results show APES performs best with respect to bias and standard deviation. Along-track interferometry (ATI) and topographic interferograms are presented to visually demonstrate performance improvements.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"406-416"},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535468","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

Prototype Features Driven High-Performance Few-Shot Radar Active Jamming Recognition

IEEE Transactions on Radar Systems Pub Date : 2025-02-14 DOI: 10.1109/TRS.2025.3542410

Hongping Zhou;Xiaomin Cai;Peng Peng;Zhongyi Guo

{"title":"Prototype Features Driven High-Performance Few-Shot Radar Active Jamming Recognition","authors":"Hongping Zhou;Xiaomin Cai;Peng Peng;Zhongyi Guo","doi":"10.1109/TRS.2025.3542410","DOIUrl":"https://doi.org/10.1109/TRS.2025.3542410","url":null,"abstract":"Accurate identification of jamming is the premise of effective work of radar anti-jamming systems. As the electromagnetic environment becomes increasingly complex, radar detection faces not only the issue of insufficient training samples but also the challenge of imbalanced jamming samples. To solve this problem, this article proposes a few-shot recognition method of radar active jamming guided by prototype features. In this method, a pyramid structure is used to construct feature maps at different levels to integrate low-level features and high-level semantic features, so as to retain the information of the time-frequency images of the jamming signal to the maximum extent. Meanwhile, a differentiation information attention module is introduced to capture the global and local information of the feature maps and enhance the signal perception ability of the model. Finally, we propose a prototype feature extraction and fusion module to learn the prototype features of various samples and fuse them with backbone features. In view of the uneven data of the training set, the imbalanced coefficient is proposed to improve the recognition accuracy of the few-shot jamming signal in a complex electromagnetic environment. The experimental results on the jamming simulation dataset show that the proposed model has good recognition accuracy and robustness, and can handle imbalanced jamming samples. When the jamming-to-noise ratio (JNR) exceeds 2 dB, the average recognition accuracy of jamming can reach 99%. In the case of low JNR and sample imbalance, the proposed structure can effectively identify multiple small classes of jamming.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"430-440"},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553325","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

Adaptive LPD Radar Waveform Design With Generative Deep Learning

IEEE Transactions on Radar Systems Pub Date : 2025-02-14 DOI: 10.1109/TRS.2025.3542283

Matthew R. Ziemann;Christopher A. Metzler

{"title":"Adaptive LPD Radar Waveform Design With Generative Deep Learning","authors":"Matthew R. Ziemann;Christopher A. Metzler","doi":"10.1109/TRS.2025.3542283","DOIUrl":"https://doi.org/10.1109/TRS.2025.3542283","url":null,"abstract":"We propose a learning-based method for adaptively generating low probability of detection (LPD) radar waveforms that blend into their operating environment. Our waveforms are designed to follow a distribution that is indistinguishable from the ambient radio frequency (RF) background—while still being effective at ranging and sensing. To do so, we use an unsupervised, adversarial learning framework; our generator network produces waveforms designed to confuse a critic network, which is optimized to differentiate generated waveforms from the background. To ensure our generated waveforms are still effective for sensing, we introduce and minimize an ambiguity function-based loss on the generated waveforms. We evaluate the performance of our method by comparing the single-pulse detectability of our generated waveforms with traditional LPD waveforms using a separately trained detection neural network. We find that our method can generate LPD waveforms that reduce detectability by up to 90% while simultaneously offering improved ambiguity function (sensing) characteristics. Our framework also provides a mechanism to trade-off detectability and sensing performance.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"417-429"},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535467","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

Intelligent Target Detection Method for HFSWR Based on Dual-Scale Branch Fusion Network and Adaptive Threshold Control

IEEE Transactions on Radar Systems Pub Date : 2025-02-07 DOI: 10.1109/TRS.2025.3540016

Yuanzheng Ji;Aijun Liu;Shuai Shao;Changjun Yu;Xuekun Chen

{"title":"Intelligent Target Detection Method for HFSWR Based on Dual-Scale Branch Fusion Network and Adaptive Threshold Control","authors":"Yuanzheng Ji;Aijun Liu;Shuai Shao;Changjun Yu;Xuekun Chen","doi":"10.1109/TRS.2025.3540016","DOIUrl":"https://doi.org/10.1109/TRS.2025.3540016","url":null,"abstract":"High-frequency surface wave radar (HFSWR) is a crucial tool for oceanic remote sensing and surveillance; however, radar target detection is challenged by the presence of background clutter and interference. In response, this article designs a novel dual-scale branch fusion network specifically for detecting target signals in the range-Doppler (RD) spectrum. The network effectively enhances the ability to distinguish between targets and clutter by combining large-scale environmental feature sensing with small-scale target signal structure analysis. Additionally, we propose a novel detection threshold adjustment mechanism based on the RD spectrum perception network. First, an initial detection threshold is calculated using the traditional constant false alarm rate (CFAR) method. Then, the output of the softmax layer in the RD spectrum perception network is used to adjust the threshold, improving the robustness and accuracy of the detection process. The RD spectrum perception network is trained jointly using data from the Automatic Identification System (AIS) associated with HFSWR and simulated target-embedded data. Multiple validations and analyses of the proposed detection method are conducted with these datasets. Experimental results demonstrate that the proposed method has good detection performance, outperforming several other existing methods.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"379-391"},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496503","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

Stability-Enhanced Human Activity Recognition With a Compact Few-Channel mm-Wave FMCW Radar

IEEE Transactions on Radar Systems Pub Date : 2025-02-06 DOI: 10.1109/TRS.2025.3539289

Xuanyu Peng;Yaokun Hu;Ting Liu;Ying Wu;Tatsunori Saito;Takeshi Toda

{"title":"Stability-Enhanced Human Activity Recognition With a Compact Few-Channel mm-Wave FMCW Radar","authors":"Xuanyu Peng;Yaokun Hu;Ting Liu;Ying Wu;Tatsunori Saito;Takeshi Toda","doi":"10.1109/TRS.2025.3539289","DOIUrl":"https://doi.org/10.1109/TRS.2025.3539289","url":null,"abstract":"This study explores the application of a 77 GHz mm-wave frequency-modulated continuous wave (FMCW) radar system for human activity recognition (HAR). We propose a novel density-aware convex hull (DACH) algorithm specifically designed to address the challenge of point cloud sparsity, which is particularly evident when using few-channel radar systems. This algorithm combines a triple-view convolutional neural network (CNN) and long short-term memory (LSTM) models for classification. Unlike traditional methods that often overlook the impact of sparse point cloud data, our approach emphasizes the importance of maintaining robust and dense data for precise activity recognition. Our experiments, which involved classifying nine human activities—standing, sitting, squatting, lying on the floor, transitions between these postures, and walking—demonstrate the method’s effectiveness. By using a compact 3Tx4Rx few-channel radar, we achieve a balance among cost, size, and performance, making it suitable for practical applications like indoor health monitoring and elderly care. The proposed method achieves an average classification accuracy of approximately 99.63% across all four scenarios, marking a significant improvement over existing approaches, and shows promise for real-time applications in various fields.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"360-378"},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430444","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

Corrections to “Engineering Constraints and Application Regimes of Quantum Radar”

IEEE Transactions on Radar Systems Pub Date : 2025-02-04 DOI: 10.1109/TRS.2025.3532053

Florian Bischeltsrieder;Michael Würth;Johannes Russer;Markus Peichl;Wolfgang Utschick

引用次数: 0

TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection

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

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

Space-Domain Awareness Using Over-the-Horizon Radar

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