Common Subspace Pursuit for Distributed Compressed Sensing in Wireless Sensor Networks

We address the sparse signal reconstruction in a wireless sensor network (WSN) via distributed compressed sensing (DCS). The multiple sparse signals from WSN are modeled by the mixed-support set (MSM) model, which describes the inter-correlation of the signals by the common support set and represents the individual features by the innovation support sets. We propose a novel common subspace pursuit (CSP) algorithm to estimate the common support set, in order to reduce the reconstruction error and computing time. The results of simulations on a hierarchical clustering based WSN show that the proposed CSP algorithm is superior over the conventional algorithms in terms of both reconstruction error and runtime.