Super-Resolution Target Localization by Fusing Signals from Multiple MIMO FMCW Automotive Radars

Achieving high azimuth resolution is one of the main bottleneck for automotive radars, which generally demands a large aperture of antenna array. However, building an automotive radar system with a large antenna array is a very challenging task from the perspective of both technological readiness and cost. To circumvent this problem, we propose to fuse signals from multiple small automotive radars placed over the facade of a car as an alternative solution with low system complexity, where each radar with a small Multiple-Input Multiple-Output (MIMO) array operate independently without accurate synchronization. To (partially) coherently process the measurements from all the radars, a 2-D MUltiple Signal Classification (MUSIC) based algorithm is proposed for joint Direction-of-Arrival (DOA)-range estimation of targets in which spatial smoothing technique is exploited to tackle highly correlated signals. Taking advantage of the proposed estimation approach and multiple radars, it significantly improves the azimuth resolution of the system compared to that of a single MIMO radar. The performance of the proposed method is demonstrated through both numerical simulations and experimental results.