Joint Sparse Signal Ensemble Reconstruction in a WSN Using Decentralized Bayesian Matching Pursuit
Wireless networks comprised of low-cost sensory devices have been increasingly used in surveillance both at the civilian and military levels. Limited power, processing, and bandwidth resources is a major issue for abandoned sensors, which should be addressed to increase the network's performance and lifetime. In this paper, the framework of compressive sensing is exploited, which allows accurate recovery of signals being sparse in some basis using only a small number of random incoherent projections. In particular, a recently introduced Bayesian Matching Pursuit method is modified in a decentralized way to reconstruct a multi-signal ensemble acquired by the nodes of a wireless sensor network, by exploiting a joint sparsity structure among the signals of the ensemble.