An Efficient Method for Cooperative Multi-Target Localization in Automotive Radar

Abstract

We consider the problem of locating multiple targets using automotive radar by exploiting a pair of cooperative vehicles, which form a mono- and bi-static sensing system to provide spatial diversity for localization. Each of the two sub-systems can measure the target echoes. The problem is to determine the locations of multiple targets in the surrounding area. A conventional approach is to directly estimate the target locations from the joint distribution of the mono- and bi-static observations, which is computationally prohibitive. In this paper, we propose a efficient two-step method that first uses the delay and angle estimates from each individual system to determine initial target locations, which are subsequently refined via an association and fusion step. Specifically, we use a 2-dimensional (2-D) fast Fourier transform (FFT) based approach to obtain the delay and angle estimates of each target in a sequential manner. The delay/angle estimates obtained by mono-static and bi-static systems lead to two sets of initial target location estimates, which are then sorted and paired via a minimum distance criterion. Finally, the initial location estimates are fused/weighted according to the target strength observed by each system. Simulation results show that our cooperative approach yields significant improved performance over non-cooperative approaches using only the mono-static or bi-static sensing system.