arXiv Computer Vision
Rad-R: A Raw-ADC Radar Dataset and Capture-Invariant SSM for Hardware-Fault Diagnosis
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SCORE-LM: State-Space Radar Representations with Language Models for Fault Diagnosis
arXiv:2609.38980v1 Announce Type: new Abstract: Radar hardware faults threaten automated perception, motivating accurate, compact diagnosis and understandable maintenance guidance. We introduce SCORE...
arXiv Computer Vision
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If It Moves, Radar Knows: A Physics-Aware Radar Transformer for Class-Agnostic Moving-Object Detection
The paper introduces the Physics-Aware Radar Transformer (PART), a radar-only detector that predicts moving-object existence, surface points, and ground-plane velocity using Doppler-aware query initialization and physics-guided cross-attention. PART achieves high class-agnostic performance on the nuScenes dataset, excelling in rare categories and adverse conditions such as night, rain, and occlusion. The model is lightweight, with only 1.1 million parameters, and its code and pretrained weights will be released publicly.
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Learned Compression of SAR Phase-History Data: A Rate-Honest Feasibility Study on GOTCHA
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Selection, Not Fusion: Radar-Modulated State Space Models for Radar-Camera Depth Estimation
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Jun 24
Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics
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arXiv Machine Learning
Sep 22
On Learning Spatial Structure from Pre-Beamforming Per-Antenna Range-Doppler Radar Measurements
This study explores whether spatial structure can be learned directly from pre-beamforming per-antenna range-Doppler (RD) radar measurements, bypassing traditional beamforming steps. Using a 6‑TX × 8‑RX automotive radar with a chirp‑sequence FMCW transmit scheme, the authors train a dual‑chirp shared‑weight encoder on raw RD tensors and evaluate spatial recoverability via bird’s‑eye‑view occupancy maps. Experiments across different transmit configurations (A‑only, B‑only, A+B) and receive apertures demonstrate that meaningful spatial structure is indeed recoverable through learned spatial mixing, without hand‑crafted signal‑processing stages.