1 + 1 is Greater Than 2: Collaborative Automotive Radar Imaging Exploiting Spatial Diversity

Automotive radar provides superior performance for autonomous vehicles over camera and LiDAR in harsh environment. However, the performance of single automotive radar is subject to either small aperture or limited field of view. It is of great interests to improve the automotive radar imaging performance with an automotive radar network. In this paper, we propose a distributed iterative adaptive approach based collaborative automotive radar imaging using an automotive radar network consisting of heterogeneous automotive radars. Under the proposal, raw radar measurement is not shared. Instead, by exploiting the spatial diversity, each automotive radar fuses the intermediate local imaging estimations with adjacent neighboring radars. Numerical results demonstrate the performance improvement of the collaborative radar imaging.