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On Learning Spatial Structure from Pre-Beamforming Per-Antenna Range-Doppler Radar Measurements
Automotive radar perception pipelines commonly construct angle-domain representations via beamforming before applying learning-based models. This work instead investigates a representational question: can meaningful spatial structure be learned directly from pre-beamforming per-antenna range-Doppler
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