Sensing matrix optimization in Distributed Compressed Sensing

Distributed Compressed Sensing (DCS) seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated. In this paper we consider the scenario in which the (overcomplete) bases for common component and innovations are different. We propose and analyze a distributed coding strategy for the common component, and also the use of Efficient Projection (EP) method for optimizing the sensing matrices in this setting. We show the effectiveness of our approach by computer simulations using the Orthogonal Matching Pursuit (OMP) as joint recovery method, and we discuss the configuration of the distribution strategy.