Ayano Ueki;Robert D. Palmer;Boonleng Cheong;Sebastián M. Torres
{"title":"The “Data Challenge” for Fully Digital Phased-Array Radars: Potential of Nonuniform Quantization for Weather Applications","authors":"Ayano Ueki;Robert D. Palmer;Boonleng Cheong;Sebastián M. Torres","doi":"10.1109/TRS.2025.3545810","DOIUrl":"https://doi.org/10.1109/TRS.2025.3545810","url":null,"abstract":"Radars are essential for monitoring rapidly intensifying severe weather phenomena, enabling timely warnings and informed decision-making to protect lives and property. The WSR-88D radar network, consisting of more than 160 polarimetric radars across USA, has been recognized as one of the most reliable and highest quality weather radar networks in the world but is reaching end of life in the coming decades. To confront this challenge, phased-array radars (PARs) with their superior capability for rapid and flexible scanning are being considered as a replacement technology. Among PAR architectures, fully digital systems provide advanced capabilities and also are expected to reduce maintenance requirements and operational costs through software reconfigurability. Furthermore, fully digital PARs, which provide access to element-level in-phase (<italic>I</i>) and quadrature-phase (<italic>Q</i>) data, can perform scans with increased flexibility with the potential for adaptive beamforming. However, they can generate an enormous volume of data, presenting a significant challenge for operational use. To address this “data challenge,” this study examines the impacts of <italic>I</i>/<italic>Q</i> data quantization, both uniform and nonuniform, on spectral moments and polarimetric variables using data from “Horus,” the first fully digital phased-array weather radar developed at the Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU). The findings demonstrate that nonuniform quantization has the potential to reduce data size while maintaining data quality and dynamic range.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"498-510"},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688125","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}
Simin Zhu;Satish Ravindran;Lihui Chen;Alexander G. Yarovoy;Francesco Fioranelli
{"title":"DeepEgo+: Unsynchronized Radar Sensor Fusion for Robust Vehicle Ego-Motion Estimation","authors":"Simin Zhu;Satish Ravindran;Lihui Chen;Alexander G. Yarovoy;Francesco Fioranelli","doi":"10.1109/TRS.2025.3546001","DOIUrl":"https://doi.org/10.1109/TRS.2025.3546001","url":null,"abstract":"This article studies the problem of estimating the 2-D motion state of a moving vehicle (ego motion) using millimeter-wave (mmWave) automotive radar sensors. Unlike prior single-radar or synchronized radar systems, the proposed approach (named DeepEgo+) can achieve sensor fusion and estimate ego motion using an unsynchronized radar sensor network. To achieve this goal, DeepEgo+ combines two neural network (NN)-based components (i.e., Module A for motion estimation and Module B for sensor fusion) with a decentralized processing architecture using the late fusion technique. Specifically, each radar sensor in the network has a Module A that processes its output and computes an initial motion estimate, while Module B fuses the initial estimates from all radar sensors and outputs the final estimate. This novel architecture and fusion scheme not only eliminates the synchronization requirement but also provides robustness and scalability to the system. To benchmark its performance, DeepEgo+ has been tested using a challenging real-world radar dataset, RadarScenes. The results show that DeepEgo+ provides significant performance advantages over recent state-of-the-art approaches in terms of estimation accuracy, long-term stability, and robustness against high outlier ratios and sensor failures. Furthermore, the influence of vehicle nonzero acceleration on ego-motion estimation is identified for the first time, and DeepEgo+ demonstrates the feasibility of compensating for its effect and further improving the estimation accuracy.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"483-497"},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645338","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}
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}
{"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}
{"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}
{"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}
{"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}
{"title":"Spectral Neural Network for Specific Emitter Identification","authors":"Wenjun Yan;Qing Ling;Limin Zhang;Keyuan Yu","doi":"10.1109/TRS.2025.3539677","DOIUrl":"https://doi.org/10.1109/TRS.2025.3539677","url":null,"abstract":"The existing ResNet models used in specific emitter identification (SEI) typically use global average pooling (GAP) to reduce feature dimensions. However, this results in a substantial loss of key subtle information. In particular, the recognition performance often fails to meet SEI requirements when unbalanced and weakly labeled samples are present. This study uses the characteristics of radar emitter signals and proposes an approach for SEI based on frequency-domain pooling, fast Fourier transform (FFT) pooling, and wavelet transform pooling. First, a detailed mathematical derivation of FFT pooling and wavelet transform pooling was performed. Next, low-frequency (LF) and high recognition accuracy (HRA) selection criteria were used to select the corresponding retained frequency components. Finally, the new pooling method and frequency-component selection criteria were employed to construct a spectral neural network (SNN) framework for recognizing specific radar emitters, using ResNet as the foundation. Experiments were conducted using a real radar radiation-source dataset, and the results indicated that the proposed algorithm improved the recognition performance by nearly 5%, compared to the GAP-based algorithm, under the same conditions. Moreover, the proposed algorithm exhibited superior recognition performance and stronger robustness than the GAP method under the conditions of sample imbalance and few shot.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"695-708"},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949260","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}
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}
{"title":"Corrections to “Engineering Constraints and Application Regimes of Quantum Radar”","authors":"Florian Bischeltsrieder;Michael Würth;Johannes Russer;Markus Peichl;Wolfgang Utschick","doi":"10.1109/TRS.2025.3532053","DOIUrl":"https://doi.org/10.1109/TRS.2025.3532053","url":null,"abstract":"Presents corrections to the paper, Errata to “Engineering Constraints and Application Regimes of Quantum Radar”.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"246-246"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}