Multipath Ghost Target Identification for Automotive MIMO Radar

Abstract

We consider the problem of angle estimation and ghost target identification for automotive multiple-input multiple-output (MIMO) radar in multipath scenarios. Firstly, we establish the multipath propagation model for the case of horizental MIMO arrays, and divide the multipath into two categories, i.e., Type 1: multipath with direction-of-arrival (DOA) $\neq$ direction-of-departure (DOD); Type 2: multipath with DOA$=$DOD. In the presence of multipath, the different DOA and DOD angles corrupt the notion of virtual array for MIMO radar, making angle estimation a major challenge. To jointly estimate the DOA and DOD of the target reflections, including both the direct path and multipath scenarios, we introduce a multipath iterative adaptive approach (MP-IAA), which possesses the super resolution, low sidelobe level, and robust properties for DOA and DOD estimation. Then, the Type 1 multipath with DOA$\neq$DOD can be directly identified based on the MP-IAA’s DOA and DOD estimates. Regarding to the Type 2 multipath with DOA$=$DOD, we solve the triangle relationships to identify the corresponding ghost targets. Numerical examples are provided to demonstrate the effectiveness of the proposed algorithm for angle estimation and ghost target identification using automotive MIMO radar.