Model-based decentralized Bayesian algorithm for distributed compressed sensing

In this paper, a novel model-based distributed compressive sensing (DCS) algorithm is proposed. DCS exploits the inter-signal correlations and has the capability to jointly recover multiple sparse signals. Proposed approach is a Bayesian decentralized algorithm which uses the type 1 joint sparsity model (JSM-1) and exploits the intra-signal correlations, as we l l as the inter-signal correlations. Compared to the conventional DCS algorithm, which only exploit the joint sparsity of the signals, the proposed approach takes the intra-and inter-scale dependencies among the wavelet coefficients into account to enable the utilization of the individual signal structure. Furthermore, the Bessel K-form (BKF) is used as the prior distribution which has a sharper peak at zero and heavier tails than the Gaussian distribution. The variational Bayesian (VB) inference is employed to perform the posterior distributions and acquire a closed-form solution for model parameters. Simulation results demonstrate that the proposed algorithm have good recover y performance in comparison with state-of the-art techniques.

Automated summary

This paper proposes a novel model-based distributed compressive sensing algorithm that exploits inter-signal correlations and can recover multiple sparse signals simultaneously. The algorithm uses a Bayesian decentralized approach and a joint sparsity model to utilize signal structures effectively.