On Learning Spatial Structure from Pre-Beamforming Per-Antenna Range-Doppler Radar Measurements

תקציר מקורי באנגליתarXiv:2604.01921v3 Announce Type: replace-cross Abstract: 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 (RD) measurements? Experiments are conducted on a 6-TX x 8-RX (48 virtual antennas) commodity automotive radar employing an A/B chirp-sequence frequency-modulated continuous-wave (CS-FMCW) transmit scheme, in which the effective transmit aperture varies between chirps (single-TX vs multi-TX), enabling controlled analyses of chirp-dependent transmit configurations. We operate on pre-beamforming per-antenna RD tensors using a dual-chirp sh