Joint sparse modeling for target parameter estimation in distributed MIMO radar

Distributed compressive sensing (DCS) gives the method for sparse multi-signal ensemble processing. Distributed MIMO radar provides spatial diversity by viewing the targets from different angles to detect stealth targets. In this paper we apply DCS to distributed MIMO radar and propose a joint sparse modeling to get the sparse representation of the received signal ensemble. We develop Joint-OMP algorithm to reconstruct the signal ensemble. Moreover, simulations demonstrate accurate reconstruction from fewer samples than that required by Nyquist theory. And extensive numerical experiments demonstrate that with the same number of samples, processing the signal ensemble simultaneously is more effective and more accurate than processing signals in each receiver with CS separately.