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 work, 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. The proposed approach requires a minimal amount of data transmissions among the sensors and a central node thus preserving the sensors´ limited resources. At the same time, it achieves a reconstruction performance comparable to other distributed compressive sensing methods, which require the communication of a whole set of measurements to the central node.