Multipath Suppression Using 2-D Angle Analysis Based on the MIMO FMCW Radar

Indoor human detection is a key prerequisite for realizing smart home systems. However, radar sensors for human detection experience severe performance degradation due to multipaths of indoor environments. Multipath signals generate ghost targets that highly deteriorate radar system stability. In this study, we analyze the limitations of the conventional multiple-input and multiple-output (MIMO) beamforming method using the virtual antenna array by considering two failure cases with respect to multipath suppression. To overcome such limitations of the conventional beamforming, a path matching algorithm is proposed based on the MIMO frequency-modulated continuous-wave (FMCW) radar. The path matching algorithm leverages the angle properties of signal paths to identify direct paths and the first-order multipath pairs. It utilizes 2-D angle analysis by considering the angle of departure (AoD) and angle of arrival (AoA) separately and also addressing the angle ambiguity problem caused by large antenna spacing. Experimental results verify that the proposed algorithm has the superior detection performance, in terms of both the number of ghost targets and missed targets, compared with the conventional beamforming method using the virtual antenna array.