Advanced SAR Imaging Techniques

Últimos artículos

• Cross-Dataset Transfer and Unknown-Class Detection in Imbalanced SAR Ship Classification

  Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, Giulio Del Corso · 29 de septiembre de 2026

  Ship classification from Synthetic Aperture Radar (SAR) imagery is a critical computer vision task, yet the robustness of models under deployment shifts remains unclear. While models are often trained on one dataset and deployed on another, we lack a comprehensive understanding of their cross-datase…

• Privacy-Preserving Full-Body Meshing from mmWave Radar via Mesh Foundation Model Supervision

  Shuxing Zhang, Yongquan Ni, Zhenyu Ding, Yawen Lin · 29 de septiembre de 2026

  Millimeter-wave (mmWave) radar enables privacy-preserving human perception, but the extreme sparsity of point clouds from commercial single-chip sensors (mean ~6.5 points/frame; ~28% empty frames) has confined prior art to body-part keypoints or discrete action classification. We present a cross-mod…

• ContraFM-S2O: Flow Matching-Based One-step SAR-to-Optical Image Translation Model with Contrastive Learning

  Mingqian Yu, Wei-kuan Chiang, Qiurui Wang, Peilin Zhao · 28 de septiembre de 2026

  In recent years, diffusion models and GAN-based models have become the mainstream approaches for SAR-to-optical image translation, owing to their advantages, such as high-quality generation and stable training. However, they have shortcomings such as high inference latency and the generated optical …

• ESAFusion: LiDAR--4-D Radar Fusion via Local Geometric Complementation and Multiscale Adaptive Interaction for 3-D Object Detection

  Gang Ma, Senjie Hu, Junjie Liu, Chao Wang, Hui Wei · 25 de septiembre de 2026

  LiDAR--4-D radar fusion combines accurate spatial geometry with motion and reflectivity cues from radar, offering a promising solution for 3-D object detection in complex driving environments. However, sparse radar observations and differences in spatial sampling between the two modalities complicat…

• SARFusion: Scene-Aware Routing Fusion for Robust Camera-LiDAR 3D Object Detection

  Yuting Zhao, Ziyi Zheng, Shuxiao Li · 25 de septiembre de 2026

  Camera-LiDAR fusion has become a prevailing paradigm for 3D object detection in autonomous driving. However, existing fusion detectors often establish strong inter-modality dependencies by decoding object queries from tightly coupled multimodal representations. Under corrupted driving conditions, su…

• Automotive mmWave Spinning Radar Place Recognition with Spatially Gated Feature-Correlation Representation

  Saimunur Rahman, Sagun Singh Shrestha, Abdelwahed Khamis, Peyman Moghadam · 24 de septiembre de 2026

  Automotive spinning FMCW radar provides dense, $360^\circ$ sensing and remains reliable under poor illumination and adverse weather, making it well-suited to autonomous navigation. Place recognition uses these observations to identify previously visited locations for re-localization and long-term na…

• SGDet3D++: Geometry-Grounded Semantics for 4D Radar and Camera 3D Object Detection

  Xiaokai Bai, Zhenyu Fan, Lianqing Zheng, Songkai Wang, Si-Yuan Cao, Hui-liang Shen · 24 de septiembre de 2026

  4D radar complements dense image semantics with long-range geometry and radial motion, but existing radar--camera detectors largely solve \emph{where} to align the modalities while leaving \emph{whether} a piece of evidence supports an evolving object hypothesis implicit. An image token may describe…

• A Systematic Evaluation of Infrastructure-Based Radar System for Highway Traffic Monitoring

  Tianheng Zhu, Woei-chyi Chang, Alamss Riaz, Sogand Hasanzadeh, Yiheng Feng · 24 de septiembre de 2026

  Infrastructure-based radar systems offer robust and long-range solutions for traffic monitoring, yet their detection and tracking performance under real-world conditions remains insufficiently evaluated. This study introduces DRaT (Drone and Radar Trajectories), a dual-modality dataset of naturalist…

• Physics-guided deep metric learning with continuous time embeddings for open-world radar pulse de-interleaving

  Vikas Agnihotri, Jasleen Kaur · 23 de septiembre de 2026

  Radar pulse de-interleaving is a foundational Electronic Support Measures (ESM) task that aims to separate chronologically interleaved pulse streams from multiple non-cooperative transmitters under unknown emitter cardinality in dense, contested electromagnetic environments. Classical histogram tran…

• STAR: Scene- and Task-Aware 4D Radar Preprocessing Towards End-to-End Cognitive Radar

  Seung-Hyun Song, Dong-Hee Paek, Seung-Hyun Kong · 22 de septiembre de 2026

  Four-dimensional (4D) Radar has emerged as a key sensor for environmental perception, providing range, azimuth, elevation, and Doppler measurements while remaining robust to illumination changes and adverse weather conditions. However, conventional Radar preprocessing methods, such as constant false…

• Image Frame Dynamic Object Segmentation and Ego Motion Estimation using Radar Image Fusion

  Astik Srivastava, Suhani Grover, Avinash Sharma, Madhava Krishna · 22 de septiembre de 2026

  Dynamic object segmentation and ego-motion estimation are closely coupled problems in autonomous driving, as accurate ego-motion estimation typically requires static scene observations, while identifying static observations requires knowledge of the ego motion. We present Radar-Dot, a radar--RGB fra…

• Do Spinning Radar Doppler Velocity Measurements Improve Vehicle Detection and Tracking?

  Eric Xie, Daniil Lisus, Timothy D. Barfoot · 21 de septiembre de 2026

  Spinning frequency-modulated continuous-wave (FMCW) radars have been gaining popularity in autonomous vehicle perception on account of their robustness to adverse weather conditions and 360{\deg} field of view. Recently, scanning radars have also been shown capable of generating per-azimuth Doppler …

• PDA++: Field-Aligned Planning and Scene-Adaptive Insertion in Remote Sensing

  Xianchi Dong, Yingyan Hou, Chao Ren, Wanxuan Lu, Zihan Wei, Hongfeng Yu, Yixiao Wang, Chubo Deng, Xian Sun · 18 de septiembre de 2026

  Remote sensing recognition is often constrained by scarce observations of rare targets and costly annotations, making realistic synthetic augmentation particularly valuable for few-shot and long-tailed scenarios. Object insertion provides an efficient way to increase target diversity while preservin…

• 4D Radar Perception Algorithms for Autonomous Driving: A Review

  Xumin Wu, Jun Zhou, Jilin Mei, Chen Min, Yu Hu · 18 de septiembre de 2026

  Research on 4D millimeter-wave radar perception algorithms has flourished in recent years, extending from signal processing and object detection to semantic segmentation, motion estimation, occupancy prediction, and dynamic scene reconstruction. This review organizes the field according to the evolu…

• IMM-based Multiple Object Tracking using a State Prediction Neural Network

  Chan-Bin Lim, Dong-Hee Paek, Seung-Hyun Kong · 15 de septiembre de 2026

  Object tracking is essential for autonomous vehicles to avoid obstacles and plan routes. Radar maintains detection performance even in adverse weather and can measure relative velocity through the Doppler effect, making it well suited for object tracking. In this paper, we propose a data-driven stat…

• RF-VoID: Towards Bandwidth-Efficient Exterior Tile Void Detection via Narrowband Radio-Frequency Representation Learning

  Xinyan Chen, Ruiqin Ma, Shunsuke Shoda, Changyu Zhou, Ryo Natsuaki, Akira Hirose, Jianfei Yang, Li Yi · 14 de septiembre de 2026

  Hidden debonding behind exterior ceramic tiles is a falling-tile hazard, and millimeter-wave radar offers a non-contact way to find it. Conventional interpretation first reconstructs a range profile, so its reliability is bounded by the available bandwidth, yet bandwidth is what sets the cost, the a…

• 3D Point Splatting for mmWave Radar Novel View Synthesis

  Adnan Armouti, Yixuan Gao, Rajalakshmi Nandakumar · 11 de septiembre de 2026

  Solving novel view synthesis (NVS) for millimeter-wave (mmWave) radar requires a renderer that is physically faithful, complex-valued, and multi-viewpoint-tractable. No prior method achieves these three properties simultaneously. Differentiable Monte Carlo (MC) ray tracers implement the radar forwar…

• GRADE: Single-Frame Generative Radar Depth Estimation Under Visual Degradation

  Bin Zhao, Patrick Chiou, Nakul Garg · 11 de septiembre de 2026

  Dense 3D depth perception fails under smoke, fog, and darkness because optical sensors cannot penetrate airborne particulates. mmWave radar remains usable and measures range accurately under these conditions, but its small aperture limits angular resolution. We present GRADE, which grounds a pretrai…

• Rad-R: A Raw-ADC Radar Dataset and Capture-Invariant SSM for Hardware-Fault Diagnosis

  Mainak Mallick, Junghwan Yim, Alankrit Gupta, Seung-Kyum Choi · 10 de septiembre de 2026

  Automotive mmWave radar can develop vibration, antenna misalignment, radome blockage, and receive-channel degradation that corrupt the signal before perception begins. Data for these faults are scarce because each condition must be induced and measured on physical hardware. We introduce Rad-R, a raw…

• Segment Any Motion with Radar: Robust Multimodal Moving-Object Segmentation and Tracking

  Jue Wang, Xuan Wang, Hao Zhou, Ruixiang Zhou, Yixuan Zhou, Tianshuo Yuan, Jieming Ma, Jie Zhang, Fei Luo · 9 de septiembre de 2026

  Moving-object perception must decide which image regions correspond to real motion and keep every instance identified over time. Methods that read motion from appearance, optical flow, or estimated trajectories lose that evidence under poor illumination, adverse weather, reflections, and occlusion. …

• SED-FOD: Scattering-Aware Expert Decomposition for Few-Shot Cross-Sensor SAR Object Detection

  Shu Yang, Zhen Chen, Zhiyu Jiang, Yanlei Li, Xingdong Liang · 20 de agosto de 2026

  Synthetic aperture radar (SAR) object detection is an important part of remote sensing interpretation. However, because of variations in frequency band, resolution, background clutter, and target scattering responses, the performance of existing detectors often degrades when training and testing dat…

• MITE-Net: SWaP-Optimized 4K Video Tiny Target Perception for Embodied Edge SAR

  Mingshuo Xu, Mu Hua, Jigen Peng, Qi Wang, Shigang Yue · 18 de agosto de 2026

  Real-time tiny target perception in high-resolution imagery is critical for embodied Search-and-Rescue (SAR) missions. However, strict Size, Weight, and Power (SWaP) constraints on edge devices like UAVs create a bottleneck: traditional image downsampling causes severe feature loss, while slice-base…

• Weakly Supervised Polar Low Segmentation in Sentinel-1 SAR Imagery

  Andrea Federici, Jakob Grahn, Giacomo Boracchi, Filippo Maria Bianchi · 17 de agosto de 2026

  Polar lows are intense maritime cyclones that form rapidly at high latitudes. Deep learning can detect them in Synthetic Aperture Radar (SAR) imagery, but pixel-level segmentation remains an open challenge. No pixel-level masks are available for training, and a polar low's extent is inherently subje…

• Can Language Models Understand mmWave Data? Benchmarking Large Language Models for mmWave Radar-Based Human Understanding

  Jeongwan Shin, Jaehyeon Kim, Donguk Ko, Jaeho Choi · 17 de agosto de 2026

  Large language models (LLMs) have shown remarkable reasoning and generative capabilities, motivating their use as universal reasoning engines for perception. While modern approaches such as vision-language models (VLMs) have attempted to incorporate reasoning capabilities into visual sensing, the in…

• RbFT-Net: Rectify-Before-Fuse Temporal Radar Anchors for 4D Radar-Camera Depth Completion

  Wentao Zhao, Shouxuan Wu, Yongtao Cen, Tianchen Deng, Yuyang Zhang, Jingchuan Wang · 14 de agosto de 2026

  Dense metric depth prediction from cameras and millimeter-wave radar offers a cost-effective sensing solution for autonomous systems. However, radar measurements are inherently sparse and susceptible to clutter, multipath reflections, and projection errors. While aggregating multiple radar frames pr…