IEEE Transactions on Radar Systems最新文献_第6页

Autoregressive Models for Fast Breathing Rate Estimation With Distributed Elevated Radars 分布式高架雷达快速呼吸速率估计的自回归模型

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-02-24 DOI: 10.1109/TRS.2026.3667210

Mareike Wendelmuth;Alexander Yarovoy;Francesco Fioranelli

{"title":"Autoregressive Models for Fast Breathing Rate Estimation With Distributed Elevated Radars","authors":"Mareike Wendelmuth;Alexander Yarovoy;Francesco Fioranelli","doi":"10.1109/TRS.2026.3667210","DOIUrl":"https://doi.org/10.1109/TRS.2026.3667210","url":null,"abstract":"The problem of contactless accurate monitoring of vital signs of patients in psychiatric settings is addressed in this work. Most state-of-the-art radar-based approaches rely on measurements acquired at short distances with the sensor positioned directly in front of the subject’s chest, typically within 1 m, to ensure a strong line-of-sight signal. In contrast, we investigate a substantially more challenging and clinically safer configuration, where multiple frequency-modulated continuous wave (FMCW) radars are mounted at ceiling height in a tilted position, remaining completely out of reach and outside the direct line of sight of patients. An experimental dataset involving 30 participants performing seven activities was collected using two of these ceiling-mounted radars and one reference radar mounted at hip height. A new processing pipeline is proposed with automatic range bin selection and fast autoregressive (AR) spectral estimation, including an approach that leverages all chirps per frame to improve performances with short observation window lengths. Despite the unfavorable sensing geometry and increased radar-to-subject distance, the results demonstrate that ceiling-mounted radars achieve mean absolute respiration rate errors comparable to the hip height reference radar. The best configuration yields a mean absolute error (MAE) of 2.18 respirations per minute (rpm) for participants in a sitting position at distances exceeding 4 m from the ceiling-mounted radars. Moreover, the proposed AR-based method significantly improves estimation accuracy for short windows of 3.1 s, achieving a MAE below 4.3 rpm for all radars and participant positions.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"613-626"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557391","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

Dynamic Anchor Collaborative Clustering Method for Open-Set Specific Emitter Identification 开放集特定发射器识别的动态锚协同聚类方法

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-03-13 DOI: 10.1109/TRS.2026.3673678

Kangsheng Liu;Wenjun Yan;Limin Zhang;Yi Zhang

{"title":"Dynamic Anchor Collaborative Clustering Method for Open-Set Specific Emitter Identification","authors":"Kangsheng Liu;Wenjun Yan;Limin Zhang;Yi Zhang","doi":"10.1109/TRS.2026.3673678","DOIUrl":"https://doi.org/10.1109/TRS.2026.3673678","url":null,"abstract":"Specific emitter identification (SEI) is a critical technology in electronic warfare, enabling the identification of individual emitters by leveraging their unique, physically induced subtle characteristics. Consequently, SEI has attracted considerable research interest in recent years. However, the practical deployment of conventional closed-set recognition models is severely limited by their inability to dynamically recognize unknown emitters. To address this challenge, this study proposes an open-set recognition framework based on dynamic anchor collaborative clustering (DACC). The proposed approach first employs multiscale feature extraction, extracting hierarchical features of pulse signals by integrating both local fine-grained details and global temporal feature patterns. This approach facilitates the robust training of a closed-set recognition model. Subsequently, a deep separation class anchors clustering (DSCAC) technique is presented, which initializes anchors through K-means clustering and enhances the original CAC loss with a coverage regularization term. This enhancement ensures that the anchor set effectively represents intraclass variability. Finally, a confidence-adaptive risk optimization threshold algorithm is developed, which formulates a comprehensive risk function that penalizes false rejections, false acceptances, and extreme thresholding within a risk-sensitive decision-making framework. By systematically optimizing this risk objective, the algorithm dynamically determines the optimal decision threshold, thereby enhancing the decision robustness and generalization capability of the model under distributional uncertainty. Experimental validation on real-world radar dataset and ADS-B dataset demonstrates that the proposed algorithm achieves high accuracy in rejecting unknown emitters, while maintaining excellent real-time performance and strong adaptability, thereby highlighting its strong potential for practical operational deployment.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"661-676"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557410","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

High-Precision Synchronization for 77-GHz Radar Networks via White Rabbit 基于白兔的77 ghz雷达网络高精度同步

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-04-21 DOI: 10.1109/TRS.2026.3684950

Muhammad Hashsham Chishti;Rossen Michev;Titan Yuan;Christian Waldschmidt

{"title":"High-Precision Synchronization for 77-GHz Radar Networks via White Rabbit","authors":"Muhammad Hashsham Chishti;Rossen Michev;Titan Yuan;Christian Waldschmidt","doi":"10.1109/TRS.2026.3684950","DOIUrl":"https://doi.org/10.1109/TRS.2026.3684950","url":null,"abstract":"Achieving coherent processing in advanced 77-GHz automotive radar networks requires highly precise synchronization of distributed sensors. Existing wired synchronization methods, however, incur high cabling costs and present challenges in complexity and scalability. This article proposes a novel application of White Rabbit (WR) synchronization, an Ethernet-based technology providing sub-nanosecond accuracy, to address these limitations. WR enables simultaneous synchronization and data transfer over a standard Ethernet link, thus eliminating the need for a dedicated custom clock distribution network. Two modifications of the algorithm were developed and experimentally validated with 77-GHz radar sensors. Furthermore, a comparative analysis of the synchronization requirements for different radar waveforms is presented. WR synchronization demonstrated precise frequency alignment and notably reduced peak broadening compared to uncoupled networks. Its synchronization performance is shown to be comparable to low-frequency (LF) coupled systems, while offering enhanced scalability and simplified integration. The proposed methods in this work have significant potential for enabling advanced, cost-efficient synchronized radar network architectures.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"897-910"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796156","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

Reduced Cyclostationarity MIMO Radar Waveform Synthesis Using Generative Deep Learning 基于生成深度学习的低循环平稳MIMO雷达波形合成

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-04-03 DOI: 10.1109/TRS.2026.3680475

Vesa Saarinen;Visa Koivunen

{"title":"Reduced Cyclostationarity MIMO Radar Waveform Synthesis Using Generative Deep Learning","authors":"Vesa Saarinen;Visa Koivunen","doi":"10.1109/TRS.2026.3680475","DOIUrl":"https://doi.org/10.1109/TRS.2026.3680475","url":null,"abstract":"In modern radar applications such as multiple-input multiple-output (MIMO) and cognitive radar, large apertures transmitting a large number of waveforms simultaneously are employed. Waveforms transmitted from different antennas are expected to have desirable properties for radar tasks individually, but also relative to each other to avoid mutual interference. However, existing waveform families are mostly well-known and limited in number of waveforms. In this article, novel MIMO radar waveform synthesis approaches stemming from generative deep learning are developed. The developed approaches have the potential to discover and generate large numbers of new waveforms on demand with desirable properties. In MIMO radar applications, such properties include close to ideal ambiguity function (AF), constant modulus, and low cross correlation or orthogonality among the waveforms. In some cases, a particular correlation structure is required to synthesize an arbitrary beampattern. In this article, a regularized objective for generative adversarial network (GAN)-based learning is introduced to generate low probability of intercept/detection (LPI/LPD) waveforms with reduced cyclostationarity (CS), while preserving the desirable properties for radar tasks. The capability to generate new waveforms on demand makes it more difficult for the adversary to detect or classify the waveforms employed while the performance of the radar system is not impacted. Moreover, a method where the phase of the training data samples may be dithered to reduce CS over the code period is presented. The simulation results demonstrate that the synthesized MIMO radar waveforms possess the desired properties with high fidelity while having improved LPI/LPD properties because of reduced CS.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"829-840"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11474626","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147736994","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}

引用次数: 0

2-D Root-MUSIC Approach for Delay–Doppler Parameter Estimation for DFRC–OTFS Modulation DFRC-OTFS调制延迟-多普勒参数估计的二维根- music方法

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-04-27 DOI: 10.1109/TRS.2026.3687576

Akshay S. Bondre;Christ D. Richmond;Nicolò Michelusi

{"title":"2-D Root-MUSIC Approach for Delay–Doppler Parameter Estimation for DFRC–OTFS Modulation","authors":"Akshay S. Bondre;Christ D. Richmond;Nicolò Michelusi","doi":"10.1109/TRS.2026.3687576","DOIUrl":"https://doi.org/10.1109/TRS.2026.3687576","url":null,"abstract":"Orthogonal time–frequency space (OTFS) is a modulation scheme with properties that are particularly well-suited for integrated sensing and communications (ISAC). The structure of the scheme enables transmission of a dual-function radar communications (DFRC) OTFS frame consisting of pilot and data symbols, with the flexibility to select the pilot symbols to form a desirable sensing radar waveform. In this article, a 2-D root-multiple signal classification (MUSIC) approach is presented to estimate the delays and Doppler shifts corresponding to multiple scatterers using such an OTFS waveform. First, the MUSIC approach for a single-pilot symbol is presented, along with a discussion highlighting the challenges in extending this method to the multiple-pilot scenario. Such an extension proves to be important in practical scenarios since multiple-pilot symbols can provide coherent integration gain for delay–Doppler (DD) estimation, enabling the transmitted waveform to have a lower peak-to-average power ratio (PAPR). Thus, the proposed method is extended for multiple pilots, and the widely used linear frequency-modulated (LFM) chirp pilot pulse is shown to possess desirable properties for applying the proposed MUSIC approach. Cramér–Rao bounds (CRBs) are derived and used as a benchmark for the performance of the 2-D MUSIC algorithm. The numerical results demonstrate that a DFRC OTFS frame with an LFM pilot sequence can achieve the same estimation accuracy with a lower PAPR as compared to an OTFS frame with a single-pilot symbol.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"983-998"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171570","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

Robust Joint Doppler Disambiguation and Phase-Compensated DOA Estimation for FMCW-TDM MIMO Radar FMCW-TDM MIMO雷达鲁棒联合多普勒消歧和相位补偿DOA估计

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-06-25 DOI: 10.1109/TRS.2026.3707122

Sen Yuan;Changheng Li;Francesco Fioranelli

{"title":"Robust Joint Doppler Disambiguation and Phase-Compensated DOA Estimation for FMCW-TDM MIMO Radar","authors":"Sen Yuan;Changheng Li;Francesco Fioranelli","doi":"10.1109/TRS.2026.3707122","DOIUrl":"https://doi.org/10.1109/TRS.2026.3707122","url":null,"abstract":"This study addresses the combined challenges of reduced maximum unambiguous Doppler velocity and DOA estimation blurring inherent in time-division multiplexing (TDM) multi-input–multi-output (MIMO) frequency-modulated continuous wave (FMCW) radar. We propose a novel joint de-aliasing framework that integrates peak searching via matched filtering with a compensated direction-of-arrival (DOA) processing scheme. A key advantage of the proposed method is its versatility: it can work with general MIMO configurations without the requirement of overlapped virtual elements, whereas existing state-of-the-art methods typically necessitate specific virtual array configurations. The framework efficacy is validated through numerical simulations to establish theoretical performance, experimental data from a radar target simulator (RTS) utilizing an irregular MIMO topology based on Texas instruments (TIs) cascaded radar boards, and real-world driving scenarios to demonstrate practical robustness. Results indicate that the proposed method achieves an estimation error comparable to state-of-the-art benchmarks—within a 1% margin in RTS experiments—while providing reliable target localization in complex dynamic environments.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1212-1225"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148484806","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

Position and Velocity Estimation Accuracy in MIMO–OFDM Radar Sensor Networks: A Fisher Information Analysis MIMO-OFDM雷达传感器网络的位置和速度估计精度：Fisher信息分析

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-06-22 DOI: 10.1109/TRS.2026.3705988

Lorenzo Pucci;Luca Arcangeloni;Andrea Giorgetti

{"title":"Position and Velocity Estimation Accuracy in MIMO–OFDM Radar Sensor Networks: A Fisher Information Analysis","authors":"Lorenzo Pucci;Luca Arcangeloni;Andrea Giorgetti","doi":"10.1109/TRS.2026.3705988","DOIUrl":"https://doi.org/10.1109/TRS.2026.3705988","url":null,"abstract":"Motivated by the growing adoption of orthogonal frequency division multiplexing (OFDM) waveforms in radar-capable sensing platforms, including emerging OFDM-based integrated sensing and communication (ISAC) architectures, this article develops a Fisher-information framework to characterize fundamental limits on target position and velocity estimation in cooperative, distributed multiple-input multiple-output (MIMO)–OFDM sensing networks. Assuming that target reflections are well separated in the delay–Doppler–angle domain, we consider a single point-like target to enable a tractable Fisher information matrix (FIM) characterization. Closed-form Cramér–Rao lower bounds (CRLBs) are derived for monostatic and bistatic sensing links and then unified into a common framework for cooperative deployments, including coordinated monostatic networks, multistatic configurations, and heterogeneous combinations thereof. The resulting bounds enable joint estimation of target position and velocity from network measurements and reveal how estimation accuracy depends on the number of sensing nodes, waveform bandwidth, antenna-array size, and network geometry. The numerical results validate the analysis, quantify the gains and robustness brought by cooperation over a region of interest (RoI), and show that localization- and velocity-oriented objectives may favor different subsets of cooperating nodes. Overall, the proposed framework provides a principled tool for metric-aware node selection and sensing-side design, dimensioning, and time–frequency resource partitioning in future OFDM-based ISAC systems.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1226-1248"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11574502","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148485736","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}

引用次数: 0

mmFallBbox: An Effective and Efficient Fall Detection Framework With a Comprehensive Benchmark mmFallBbox：一个具有综合基准的有效和高效的跌倒检测框架

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-07-20 DOI: 10.1109/TRS.2026.3715244

Wenxuan Li;Dongheng Zhang;Jianwen Tong;Rui Zhang;Yang Hu;Qibin Sun;Yan Chen

{"title":"mmFallBbox: An Effective and Efficient Fall Detection Framework With a Comprehensive Benchmark","authors":"Wenxuan Li;Dongheng Zhang;Jianwen Tong;Rui Zhang;Yang Hu;Qibin Sun;Yan Chen","doi":"10.1109/TRS.2026.3715244","DOIUrl":"https://doi.org/10.1109/TRS.2026.3715244","url":null,"abstract":"Accidental falls among the elderly demand highly reliable detection systems; however, existing solutions based on wearables, cameras, or WiFi often suffer from environmental interference, privacy concerns, and poor performance in detecting slow-onset falls (e.g., fainting). Despite these challenges, millimeter-wave (mmWave) radar-based methods have emerged as an attractive alternative. In this article, we propose an effective and efficient fall detection method, mmFallBbox, which leverages mmWave radar to track 3-D human bounding box dynamics. By exploiting the correlation between fall states and bounding box evolution, our approach effectively distinguishes between normal activities and slow fall states, such as fainting or medical conditions, which traditional systems struggle to detect. To evaluate the performance of mmFallBbox, we collected a large-scale fall detection dataset consisting of 60 h of radar data synchronized with video annotations. This dataset will be made publicly available to the research community for further development. We achieved an <inline-formula> <tex-math>$F1$ </tex-math></inline-formula> score of 0.977 on our dataset and achieved state-of-the-art performance with limited computational complexity. Moreover, extensive experimental results show significant improvements in detecting slow-onset falls and providing explainable outputs, offering a promising solution for real-world fall detection applications.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1349-1360"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747857","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

Fully Randomized Stepped-Carrier OFDM Radar for Integrated Sensing and Communication Systems 用于综合传感与通信系统的全随机阶跃载波OFDM雷达

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-08-14 DOI: 10.1109/TRS.2026.3724109

Mingzhe Bian;Thomas Ranstrom;Hüseyin Arslan;Gökhan Mumcu

{"title":"Fully Randomized Stepped-Carrier OFDM Radar for Integrated Sensing and Communication Systems","authors":"Mingzhe Bian;Thomas Ranstrom;Hüseyin Arslan;Gökhan Mumcu","doi":"10.1109/TRS.2026.3724109","DOIUrl":"https://doi.org/10.1109/TRS.2026.3724109","url":null,"abstract":"Integrated sensing and communication (ISAC) has recently attracted significant attention, with the design of versatile waveforms playing a central role. Stepped-carrier orthogonal frequency-division multiplexing (OFDM) is a promising ISAC waveform since it can extend the range resolution by providing access to a wider spectrum by a factor of the carrier frequency steps (<inline-formula> <tex-math>$N$ </tex-math></inline-formula>) without necessitating higher sampling requirements. However, it also inherently gets penalized in its maximum unambiguous velocity by a factor of <inline-formula> <tex-math>$N$ </tex-math></inline-formula>. This makes stepped OFDM radar unsuitable for fast-moving targets. In this article, we propose a fully randomized stepping scheme that recovers the unambiguous velocity range by spreading the power of aliasing artifacts in the velocity map, thereby extending the maximum unambiguous velocity range by a factor of <inline-formula> <tex-math>$N$ </tex-math></inline-formula>. We also propose an optimization algorithm that operates within the sequence space to minimize the aliasing artifacts in the velocity map. Moreover, we extend the framework to include the communication functionality within an ISAC system. Since the resulting overall framework is different from prior stepped OFDM schemes, we validate both radar and communication functionalities through simulations and software-defined radio (SDR)-based hardware experiments, confirming the capability of the proposed method.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1436-1451"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836851","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

SAR Anti-Interrupted-Sampling-Repeater-Jamming Method Based on Subpulse Permutation Linear Frequency Modulation 基于子脉冲排列线性调频的SAR抗干扰方法

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-08-24 DOI: 10.1109/TRS.2026.3726540

Mingyue Lou;Hongyang An;Shaoxin Wang;Shaohua Chen;Junjie Wu;Zhongyu Li;Jiangyu Yang

{"title":"SAR Anti-Interrupted-Sampling-Repeater-Jamming Method Based on Subpulse Permutation Linear Frequency Modulation","authors":"Mingyue Lou;Hongyang An;Shaoxin Wang;Shaohua Chen;Junjie Wu;Zhongyu Li;Jiangyu Yang","doi":"10.1109/TRS.2026.3726540","DOIUrl":"https://doi.org/10.1109/TRS.2026.3726540","url":null,"abstract":"To address the difficulties in detecting and extracting parameters of interrupted sampling repeater jamming (ISRJ) in synthetic aperture radar (SAR) due to strong echo masking and transform domain feature aliasing in complex land and sea scenarios, this article proposes an active–passive joint countermeasure method based on a subpulse permutation linear frequency modulated (SP-LFM) waveform. At the transmitter, the LFM signal is segmented and encoded with pseudorandom timing, creating a distinctive spectral notch structure. This design actively establishes a feature discrepancy between the jamming signal and the scene echoes within the receiving processing chain. At the receiver, a frequency-domain inverse weighting (FDIW) processing method is proposed, which significantly enhances the otherwise obscured jamming signal from the echo background. Furthermore, by utilizing the deterministic time–frequency mapping inherent in the waveform, the temporal sampling parameters of the jammer are estimated from the reconstructed jamming spectral support obtained after notch-constrained peak extraction. The estimation accuracy is further improved through multipulse joint processing. Simulation results demonstrate that the proposed method effectively detects ISRJ and extracts its parameters while preserving SAR imaging quality.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1484-1501"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925345","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