Distributed least mean squares strategies for sparsity-aware estimation over Gaussian Markov random fields

In this paper we propose distributed strategies for the estimation of sparse vectors over adaptive networks. The measurements collected at different nodes are assumed to be spatially correlated and distributed according to a Gaussian Markov random field (GMRF) model. We derive optimal sparsity-aware algorithms that incorporate prior information about the statistical dependency among observations. Simulation results show the potential advantages of the proposed strategies for online recovery of sparse vectors.