Title: Simultaneous Block-Sparse Signal Recovery Using Pattern-Coupled Sparse Bayesian Learning,
Abstract: In this paper, we consider the block-sparse signals recovery problem in the
context of multiple measurement vectors (MMV) with common row sparsity
patterns. We develop a new method for recovery of common row sparsity MMV
signals, where a pattern-coupled hierarchical Gaussian prior model is
introduced to characterize both the block-sparsity of the coefficients and the
statistical dependency between neighboring coefficients of the common row
sparsity MMV signals. Unlike many other methods, the proposed method is able to
automatically capture the block sparse structure of the unknown signal. Our
method is developed using an expectation-maximization (EM) framework.
Simulation results show that our proposed method offers competitive performance
in recovering block-sparse common row sparsity pattern MMV signals.