IEEE Transactions on Radar Systems最新文献_第5页

Relative Range-Doppler Map for Multipath Ghost Suppression in FMCW Harmonic Radar FMCW谐波雷达多径幽灵抑制的相对距离-多普勒图

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-03-07 DOI: 10.1109/TRS.2026.3691225

Feng Zhao;Yuanzhe Li;Tao Yuan;Zhenhua Liang;Weidong Hu;Hongqi Fan

{"title":"Relative Range-Doppler Map for Multipath Ghost Suppression in FMCW Harmonic Radar","authors":"Feng Zhao;Yuanzhe Li;Tao Yuan;Zhenhua Liang;Weidong Hu;Hongqi Fan","doi":"10.1109/TRS.2026.3691225","DOIUrl":"https://doi.org/10.1109/TRS.2026.3691225","url":null,"abstract":"Harmonic radar is particularly valuable in clutter-rich environments owing to its excellent clutter rejection capability. However, in frequency-modulated continuous wave (FMCW) harmonic radar systems, multipath propagation caused by strong linear scatterers generates ghosts on the range-Doppler map (RDM), which significantly increases the false alarm rate. To address this issue, we first establish a signal propagation model that explicitly incorporates the intermodulation effect between multipath signals. This model reveals that the true targets and ghosts can be categorized into four distinct types, exhibiting midpoint-symmetric relationships (MSRs) on the RDM. To leverage this property for ghost suppression, we propose a new range-Doppler domain termed the relative RDM (RRDM), defined with reference to the specific range and velocity. The proposed signal model and ghost suppression algorithm are validated by simulation and experimental results. Compared with the comparison methods, the proposed algorithm demonstrates a significant advantage in ghost suppression effectiveness. Furthermore, this study can provide a feasible solution for multipath interference mitigation in other radar systems where targets show MSRs.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"969-982"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003551","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

Pseudorandom Time-Modulated Metasurface Cloaked Target Detection Based on Encoded Modulation Sequence Reconstruction 基于编码调制序列重构的伪随机时调制超表面隐形目标检测

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-03-20 DOI: 10.1109/TRS.2026.3676208

Guonan Cui;Zhaolin Zhang;Jia Su;Chong He;Mingliang Tao;Shiyuan Li;Ling Wang

{"title":"Pseudorandom Time-Modulated Metasurface Cloaked Target Detection Based on Encoded Modulation Sequence Reconstruction","authors":"Guonan Cui;Zhaolin Zhang;Jia Su;Chong He;Mingliang Tao;Shiyuan Li;Ling Wang","doi":"10.1109/TRS.2026.3676208","DOIUrl":"https://doi.org/10.1109/TRS.2026.3676208","url":null,"abstract":"Time-modulated metasurfaces (TMMs) encoded by pseudorandom sequences (PSs) present emerging challenges to radar detection by spreading the echo spectrum and defocusing target energy after matched filtering. As a result, the radar receiver fails to reconstruct the correct range profile, severely degrading detection performance. To address this issue, this article proposes a single-channel phase-domain reconstruction framework for noncooperative 1-bit PS-TMM cloaked targets, where the metasurface elements switch between two phase states governed by a pseudorandom binary sequence. Unlike multichannel or high-dimensional solutions, the proposed method requires only one receiving channel, offering low computational complexity, low hardware cost, and ease of implementation, while maintaining high accuracy. For 1-bit PS-TMM cloaked targets with unknown range and velocity, the algorithm compensates for range–velocity coupling terms and reconstructs the encoded modulation sequence directly in the time domain. The effectiveness of the method is validated through theoretical analysis, simulations, and experimental measurements under various conditions. Results demonstrate that the proposed single-channel algorithm can effectively recover the spectral distribution of 1-bit PS-TMM echoes and restore high-resolution range profiles, showing robust performance across different modulation rates, nonideal conditions, and multitarget scenarios.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"709-729"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665391","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

Redefining Radar Segmentation: Simultaneous Static–Moving Segmentation and Ego-Motion Estimation Using Radar Point Clouds 重新定义雷达分割：同时使用雷达点云进行静态运动分割和自运动估计

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-03-30 DOI: 10.1109/TRS.2026.3678682

Simin Zhu;Satish Ravindran;Alexander G. Yarovoy;Francesco Fioranelli

{"title":"Redefining Radar Segmentation: Simultaneous Static–Moving Segmentation and Ego-Motion Estimation Using Radar Point Clouds","authors":"Simin Zhu;Satish Ravindran;Alexander G. Yarovoy;Francesco Fioranelli","doi":"10.1109/TRS.2026.3678682","DOIUrl":"https://doi.org/10.1109/TRS.2026.3678682","url":null,"abstract":"Conventional radar segmentation research has typically focused on learning category labels for different moving objects. Although fundamental differences between radar and optical sensors lead to differences in the reliability of predicting accurate and consistent category labels, a review of common radar perception tasks in automotive applications reveals that determining whether an object is moving or static is a prerequisite for most tasks. To fill this gap, this study proposes a neural network (NN)-based solution that can simultaneously segment static and moving objects from radar point clouds. Furthermore, since the measured radial velocity of static objects is correlated with the motion of the radar, this approach can also estimate the instantaneous 2-D velocity of the moving platform/vehicle (ego-motion). Notably, despite performing dual tasks, the proposed method employs very simple yet effective building blocks for feature extraction: multilayer perceptrons (MLPs) and recurrent NNs (RNNs). In addition to being the first of its kind in the literature, the proposed method also demonstrates the feasibility of extracting the information required for the dual tasks directly from unprocessed point clouds, without the need for cloud aggregation, Doppler compensation, motion compensation, or any other intermediate signal processing steps. To measure its performance, this study introduces a set of novel evaluation metrics and tests the proposed method using a challenging real-world radar dataset, RadarScenes. The results show that the proposed method not only performs well on the dual tasks but also has broad application potential in other radar perception tasks. More qualitative results can be viewed here: <uri>https://youtu.be/3ejS1chSvQ8?si=uGRugVA63BCyvNBV</uri>","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"771-786"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696676","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

RawRadarFusion: A Raw mmWave Radar Data-Fused Multimodal Object Detection Framework for Autonomous Driving RawRadarFusion：用于自动驾驶的原始毫米波雷达数据融合多模态目标检测框架

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-09-01 DOI: 10.1109/TRS.2026.3727429

Yiming Ma;Che Liu;Jiajing Wu;Mingshan Jiang;Wenming Yu

{"title":"RawRadarFusion: A Raw mmWave Radar Data-Fused Multimodal Object Detection Framework for Autonomous Driving","authors":"Yiming Ma;Che Liu;Jiajing Wu;Mingshan Jiang;Wenming Yu","doi":"10.1109/TRS.2026.3727429","DOIUrl":"https://doi.org/10.1109/TRS.2026.3727429","url":null,"abstract":"Automotive millimeter-wave (mmWave) radar is crucial for all-weather autonomous driving, making radar-camera fusion a highly promising perception solution. Conventional fusion frameworks rely on sparse radar point clouds generated via constant false alarm rate (CFAR) filtering, suffering from severe information loss. While recent raw-tensor-based approaches successfully bypass CFAR, they introduce prohibitive computational burdens and struggle with cross-modal spatial misalignments. To address these challenges, we propose RawRadarFusion, a highly efficient multimodal 3-D object detection paradigm that fundamentally shifts away from heavy tensor operations. First, we compress raw 4-D radar tensors into a lightweight motion saliency index (MSI) pseudoimage. This velocity-weighted representation preserves essential macrokinematics and elevation cues while drastically reducing data dimensionality. Second, we introduce the deformable columnwise cross-modal attention (DCCA) module, which leverages geometrically constrained learnable offsets to achieve precise spatial alignment between radar and camera features, effectively mitigating calibration drifts without the overhead of global attention. Extensive experiments on the K-Radar dataset demonstrate that RawRadarFusion not only achieves highly competitive accuracy against dense-tensor state-of-the-art models but also achieves an accuracy–efficiency tradeoff while exhibiting strong robustness against calibration error. Furthermore, migration experiments on a self-built multimodal dataset confirm its strong generalization capability, establishing it as a practical and efficient new method for industrial deployment.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1502-1515"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925443","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

Decision-Aware Waveform Design With POMDP Selection for Sequential Classification by HRRPs 基于POMDP选择的hrrp序列分类决策感知波形设计

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-08-24 DOI: 10.1109/TRS.2026.3726587

Marcel Warnke;Kilian Barth;Joachim Ender

{"title":"Decision-Aware Waveform Design With POMDP Selection for Sequential Classification by HRRPs","authors":"Marcel Warnke;Kilian Barth;Joachim Ender","doi":"10.1109/TRS.2026.3726587","DOIUrl":"https://doi.org/10.1109/TRS.2026.3726587","url":null,"abstract":"A cognitive radar benefits from adapting its transmit waveforms to the current sensing task and environment. This article presents a framework for designing waveform libraries that improve sequential target classification using high range resolution profiles (HRRPs). The closed-set classification is performed with a correlation-based classifier operating on normalized HRRPs, and the sequential selection of waveforms is formulated as a partially observable Markov decision process (POMDP), where the actions correspond to transmit waveforms or terminal class decisions and the observations are obtained from the classifier outputs. Given simulated or measured target impulse responses, we use a genetic algorithm (GA) to synthesize constant-envelope nonlinear frequency-modulated (NLFM) waveforms that influence the classifier’s performance. Two design strategies are considered: 1) a metrics-driven library, where each waveform maximizes sensitivity, precision, or <inline-formula> <tex-math>$F1$ </tex-math></inline-formula> score for a specific class and 2) a decision-aware library, where the fitness of a waveform is the maximum sensing cost at which the POMDP still selects it, directly quantifying its cost–benefit within the sequential decision process. Simulations with three and four target classes show that both libraries reduce the required number of measurements and increase the probability of correct classification up to 20.28% compared to a fusion of linear frequency modulations (LFMs). Measurements with miniaturized car models are conducted to confirm the performance gains and demonstrate robustness to moderate mismatch in aspect angle and prior information.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1467-1483"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11664484","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925444","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

Interference Mitigation for FMCW Radar Systems With Modulo Sampling Framework 基于模采样框架的FMCW雷达系统干扰抑制

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-06-10 DOI: 10.1109/TRS.2026.3702532

Peiyu Zhang;Ashish Pandharipande;Alexander Yarovoy

引用次数: 0

An Integrated Architecture for Robust End-to-End Radar Multitarget Detection in Jamming Environments 干扰环境下鲁棒端到端雷达多目标检测的集成体系结构

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-06-15 DOI: 10.1109/TRS.2026.3703613

Haoying Zhu;Kuo Liao;Jiaye Wang;Wanyou Lu

{"title":"An Integrated Architecture for Robust End-to-End Radar Multitarget Detection in Jamming Environments","authors":"Haoying Zhu;Kuo Liao;Jiaye Wang;Wanyou Lu","doi":"10.1109/TRS.2026.3703613","DOIUrl":"https://doi.org/10.1109/TRS.2026.3703613","url":null,"abstract":"When the radar suffers from the repeater deception jamming, the echoes of each target and the jamming signals will produce serious aliasing in time, frequency, and spatial domains; the real targets will be masked and the detection performance is extremely limited. To address this issue and ensure the target detection capability of the radar system in this complex jamming scenario, we propose an integrated end-to-end anti-jamming target detection architecture based on multiscale refinement and adaptive modulation (MRAM-net), which realizes the end-to-end processing from the input of the original radar echo to the output of multitarget position information when the type of jamming is unknown and the number of targets is uncertain. This integrated architecture departs from the conventional multistage serial processing paradigm, thereby mitigating error accumulation inherent in sequential processing and significantly reducing the complexity and implementation overhead of the radar signal processing system. First, we design the selective multiscale refinement block (SMRB), which effectively improves the target feature representation capability and suppresses redundant information. Furthermore, we propose a spectrogram-adaptive-modulated global aggregation (SAMGA) module, which utilizes time–frequency features as global prior information to guide and modulate the feature extraction process. Finally, we also propose a model training optimization strategy termed bidirectional spreading learning (BSL), which avoids the model falling into local optima and further improves the accuracy of the model by bidirectional spreading training. Simulation results demonstrate that MRAM-net achieves outstanding end-to-end detection performance and generalization ability across diverse jamming scenarios and varying numbers of targets.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1099-1112"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443176","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

Radar Pulse Compression With Spline-Interpolated NLFM Waveforms and Matched/Mismatched Filtering 用样条插值NLFM波形和匹配/不匹配滤波的雷达脉冲压缩

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-03-16 DOI: 10.1109/TRS.2026.3674622

Alessio Biondi;Fabrizio Argenti;Luca Facheris

{"title":"Radar Pulse Compression With Spline-Interpolated NLFM Waveforms and Matched/Mismatched Filtering","authors":"Alessio Biondi;Fabrizio Argenti;Luca Facheris","doi":"10.1109/TRS.2026.3674622","DOIUrl":"https://doi.org/10.1109/TRS.2026.3674622","url":null,"abstract":"Pulse compression is a key technique in radar systems, enabling the simultaneous achievement of high sensitivity and fine range resolution while maintaining low peak transmit power. To meet these requirements, nonlinear frequency-modulated waveforms are commonly employed. Several techniques have been proposed for designing nonlinear frequency modulation (NLFM) pulses relying on the stationary phase principle or modeling the instantaneous frequency within the pulse. In this article, we propose a model of the instantaneous frequency based on spline interpolants with fixed, nonuniformly distributed knots. We investigate several types of splines, showing that many of them allow the instantaneous frequency to be expressed as a linear function of some unknown parameters. A matched filter (MF) approach is first considered with the objective of minimizing the <inline-formula> <tex-math>$p$ </tex-math></inline-formula>-norm of the output sidelobe levels for a given range resolution. The linear formulation not only simplifies the computation of the waveform but also that of the objective function gradient, thereby enabling a more efficient optimization. We also analyze the impact of Doppler shift on the MF output and propose the use of a mismatched filter to attenuate some of the artifacts caused by its presence. Also in this case, the mismatched filter is modeled by means of spline interpolants, and an optimization procedure of its parameters is proposed. The effectiveness of the proposed methods is demonstrated by presenting several waveform examples, designed with different choices of their parameters, e.g., duration and sweep frequency, and comparing performance metrics with those of other methods in the literature.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"677-689"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11435393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148486122","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

Robust CCAA-Based MIMO Radar Architecture With Computationally Efficient Scene-Adaptive Division Multiple Access in the Presence of Target Mismatch 目标失配情况下基于ccaa的高效场景自适应多址MIMO雷达结构

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-09-15 DOI: 10.1109/TRS.2026.3733210

M. A. S. H. Kabir;M. S. Hossain;A. N. M. G. H. Rakib

{"title":"Robust CCAA-Based MIMO Radar Architecture With Computationally Efficient Scene-Adaptive Division Multiple Access in the Presence of Target Mismatch","authors":"M. A. S. H. Kabir;M. S. Hossain;A. N. M. G. H. Rakib","doi":"10.1109/TRS.2026.3733210","DOIUrl":"https://doi.org/10.1109/TRS.2026.3733210","url":null,"abstract":"Automotive MIMO radar must satisfy three competing demands at once: fine angular resolution, tolerance to steering and calibration error, and a tight vehicle-grade power envelope. Production systems use linear or rectangular arrays and run every channel regardless of traffic density, which wastes power in light scenes and caps frame rate. This article revisits the concentric circular antenna array (CCAA) and shows that its ring geometry is a natural handle for scene-driven adaptation. The proposed scene-adaptive DDMA (SADMA) scheme switches three rings around a central feed (39-Tx elements) among sparse, medium, and dense modes via a CFAR-driven classifier, activating 9, 21, or 39 channels and 96, 192, or 384 chirps per frame. The companion beamformer, scene-adaptive robust variable diagonal loading (SA-RVDL), folds mode-dependent regularization, the per-element noise floor, a condition-number snapshot-deficiency penalty, and a worst case steering-uncertainty bound into one closed-form loading factor. Over 100 Monte Carlo trials at 77 GHz, the architecture cuts total power by 32.9% (sparse) and 23.2% (medium) against a 2090-mW conventional DDMA baseline, reduces per-frame FLOPs by 93.9%, and raises the frame rate from 50 to 178.6 fps. Dense-mode SA-RVDL attains a 1.31° angle, 0.079-mm/s velocity, and 0.018-mm range RMSE, a <inline-formula> <tex-math>$2.3times $ </tex-math></inline-formula> DOA improvement over a nonrobust baseline. Block-circulant ring symmetry absorbs coupling compensation analytically, adding no arithmetic to the online path, and the shortened sparse-mode coherent processing interval (CPI) bounds acceleration-induced Doppler spread to 0.06 bin at 5 m/s2.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"4 ","pages":"1584-1591"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133632","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

First-Order Observation Modeling of Reference–Background Residuals in Single-Snapshot FDA-MIMO-GPR 单快照FDA-MIMO-GPR参考背景残差的一阶观测建模

IF 3.5

IEEE Transactions on Radar Systems Pub Date : 2026-01-01 Epub Date: 2026-09-21 DOI: 10.1109/TRS.2026.3735971

Yisu Yan;Jifeng Guo

引用次数: 0