{"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}
Mihail S. Georgiev;Aaron D. Pitcher;Timothy N. Davidson
{"title":"Classification of Radar Targets via Distribution Matching of Late-Time Resonance Parameters","authors":"Mihail S. Georgiev;Aaron D. Pitcher;Timothy N. Davidson","doi":"10.1109/TRS.2025.3559394","DOIUrl":"https://doi.org/10.1109/TRS.2025.3559394","url":null,"abstract":"A promising nonimagining approach to the classification of radar targets is to use the frequencies and attenuation rates of the resonant modes that present during a target’s late-time response (LTR) as features. Unfortunately, the estimation of these resonance parameters is rather sensitive to noise. However, we observe that when a large number of measurements of the LTR can be taken in a short time, the probability distribution of the estimates of the parameters can be estimated and then matched against a database of such distributions. That has the potential to reduce the sensitivity of the classification problem to noise. In this article, we develop a pragmatic approach to target classification using this distribution-matching approach and demonstrate its effectiveness through physical experiments. The proposed approach is shown to be highly robust to environmental clutter and somewhat robust to target orientation.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"645-655"},"PeriodicalIF":0.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892451","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":"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}
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}
{"title":"An Ultrawideband Radar Target Range-Domain Coherent Accumulation Method","authors":"He Zhou;Jianxin Wu","doi":"10.1109/TRS.2025.3575167","DOIUrl":"https://doi.org/10.1109/TRS.2025.3575167","url":null,"abstract":"To address the challenges of poor detection robustness caused by angle scintillation and the inability to achieve effective coherent accumulation in the range domain for ultrawideband (UWB) radar extended targets, this article proposes a novel single-pulse range-domain coherent accumulation method for UWB extended targets. First, the full-bandwidth signal model is approximated and converted into a fully digital array model. When full-bandwidth conditions are not met, the wideband target’s radar cross section (RCS) scattering centers are transformed into the subarray domain. The original target’s RCS phase and amplitude are reconstructed through the subarray, and phase modulation is used to adjust the beam direction, enabling a scan over the observed angles and achieving high gain on the target to obtain the maximum RCS value. Subsequently, digital beamforming (DBF) is applied to the target’s wideband range profile data to complete coherent accumulation in the range domain.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"852-863"},"PeriodicalIF":0.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323190","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}
S. Hamed Javadi;André Bourdoux;Adnan Albaba;Hichem Sahli
{"title":"A Low-Complexity PFA-Based Autofocus Algorithm for Automotive SAR","authors":"S. Hamed Javadi;André Bourdoux;Adnan Albaba;Hichem Sahli","doi":"10.1109/TRS.2025.3574010","DOIUrl":"https://doi.org/10.1109/TRS.2025.3574010","url":null,"abstract":"Radars provide robust perception of vehicle surroundings by effectively functioning in poor light and adverse weather conditions. Synthetic aperture radar (SAR) algorithms are used to address the limited angular resolution of radars by enlarging antenna aperture size synthetically as the radar moves. An autofocus algorithm is essential to improve the SAR image quality by compensating for errors mainly caused by inaccurate radar localization. Existing autofocus algorithms are mostly tailored for the frequency-domain SAR techniques which are prevalent in aviation and spaceborne applications, thanks to their lower complexity in large data processing. However, in the automotive context, the backprojection algorithm (BPA) is often preferred since it provides less distorted images at the cost of more complexity. Addressing the gap in efficient autofocus solutions for time-domain algorithms, this article introduces a dual-layered autofocus strategy that integrates the polar format algorithm (PFA) with BPA. The first layer uses a novel localization error compensation autofocus (LECA) processing pipeline to estimate and correct the localization errors within the PFA domain, leveraging its computational efficiency. The second layer seamlessly transfers these corrections to BPA, enabling high-quality SAR imaging while maintaining low complexity. In addition, the strategy extends phase gradient autofocus (PGA) techniques to enhance the efficiency of localization error compensation for BPA. Validated through real-world automotive experiments, the proposed pipeline delivers state-of-the-art image focus and resolution, setting a new benchmark for computationally efficient SAR imaging.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"799-810"},"PeriodicalIF":0.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11018462","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308501","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}
Yun Ge;Yiyu Wang;Gen Li;Ruoyi Wang;Qingwu Chen;Gang Wang
{"title":"Multipath Feature Expansion for Detection of Human Behaviors in NLOS Region Using mmWave Radar","authors":"Yun Ge;Yiyu Wang;Gen Li;Ruoyi Wang;Qingwu Chen;Gang Wang","doi":"10.1109/TRS.2025.3574571","DOIUrl":"https://doi.org/10.1109/TRS.2025.3574571","url":null,"abstract":"The ghost echoes in radar detection of a subject behaving in a nonline-of-sight (NLOS) environment can be utilized to benefit behavior recognition. Different echoes carry unique feature information due to different multipath wave incidents and scattering directions in NLOS radar detection. By fusing the ghost echo information, the recognition of subject postures behaving in the NLOS region can be enhanced. To suppress the effects of dynamic multipath noise and ensure feature extraction from as many echoes as possible, a denoising algorithm is proposed based on frequency segregation and probability estimation (FSaPE) of the time-frequency (TF) images of human behavior. To fuse the features extracted from many echoes, a multipath-based multistage input convolutional neural network (MBMI-CNN) is proposed and trained. The scheme is demonstrated by detecting people behaving behind an L-shaped corner with 77-GHz linear frequency-modulated continuous wave (FMCW) radar. It is shown that six typical postures behaving behind the corner can be successfully classified, with an average classification accuracy of 99.17% for all the postures.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"864-874"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557811","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}
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}
{"title":"A Low-Cost and Compact Software-Defined UWB Transmitter for Radar Utilizing a Nonlinear Transmission Line","authors":"Tyler Kelley;Stephen Pancrazio;Samuel Wagner;Ababil Hossain;Nhat Tran;Anh-Vu Pham","doi":"10.1109/TRS.2025.3554135","DOIUrl":"https://doi.org/10.1109/TRS.2025.3554135","url":null,"abstract":"In this article, we present a compact software-defined ultrawideband (UWB) 0.4–8.3-GHz transmitter that utilizes a nonlinear transmission line (NLTL) to expand the frequency of a transmitted pulse from a low-cost 2.5-GHz bandwidth arbitrary waveform generator (AWG). The developed transmitter consists of an AWG, amplification boards, and an NLTL. By leveraging the software-defined capabilities of the AWG and applying a digital predistortion (DPD) algorithm, we can iteratively adjust the input pulse to fine-tune and optimize the output pulse bandwidth. Ultimately, the UWB transmitter can generate software-defined pulses up to 8.3 GHz and detect 0.25-mm surface objects with a 3-dB area of 1.4 cm.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"591-598"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801012","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 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}