Low-Rank And Joint-Sparse Signal Recovery For Spatially And Temporally Correlated Data Using Sparse Bayesian Learning

In order to meet the demands of data-intensive continuous monitoring in wireless body area network, we address a structured sparse signal recovery method to exploit both spatial and temporal correlations in data using compressive sensing (CS). Using a simultaneously low-rank and jointsparse (L&S) signal model, we employ a Bayesian learning treatment by incorporating an L&S-inducing prior over the data and the appropriate hyperpriors over all hyperparameters, resulting in effective reconstruction of the L&S data. Simulation results suggest that the proposed L&S-bSBL is superior to the state-of-the-art recovery methods in terms of computation burden and runtime cost.