Ultra-Fast Accurate AoA Estimation via Automotive Massive-MIMO Radar

Massive multiple-input multiple-output (MIMO) radar, enabled by millimeter-wave virtual MIMO techniques, provides great promises to the high-resolution automotive sensing and target detection in unmanned ground/aerial vehicles (UGA/UAV). As a long-established problem, however, existing subspace methods suffer from either high complexity or low accuracy. In this work, we propose two efficient methods, accomplish fast computation accurate angle of arrival (AoA) acquisition. By leveraging randomized low-rank approximation, our multiple signal classification (MUSIC) relying on random sampling projection substantially accelerate estimation orders magnitude. Moreover, establish theoretical bounds proposed which ensure accuracy approximated pseudo-spectrum. demonstrated, pseudo-spectrum acquired fast-MUSIC would be highly precise; estimated AoA is almost as standard MUSIC. contrast, new are tremendously faster than Thus, enables real-time environmental with massive radars, has potential emerging systems.