Spatial compressive sensing in MIMO radar with random arrays

We study compressive sensing in the spatial domain for target localization using MIMO radar. By leveraging a joint sparse representation, we extend the single-pulse framework proposed in [1] to a multi-pulse one. For this scenario, we devise a tree-based matching pursuit algorithm to solve the nonconvex localization problem. It is shown that this method can achieve high resolution target localization with a highly undersampled MIMO radar with transmit/receive elements placed at random. Moreover, a lower bound is developed on the number of transmit/receive elements required to ensure accurate target localization with high probability.