Consensus-based sparse signal reconstruction algorithm for wireless sensor networks.

This article presents a distributed Bayesian reconstruction algorithm for wireless sensor networks to reconstruct the sparse signals based on variational sparse Bayesian learning and consensus filter. The proposed approach is able to address wireless sensor network applications for a fusion-center-free scenario. In the proposed approach, each node calculates the local information quantities using local measurement matrix and measurements. A consensus filter is then used to diffuse the local information quantities to other nodes and approximate the global information at each node. Then, the signals are reconstructed by variational approximation with resultant global information. Simulation results demonstrate that the proposed distributed approach converges to their centralized counterpart and has good recovery performance. [ABSTRACT FROM AUTHOR]