Simultaneous Block-Sparse Signal Recovery Using Pattern-Coupled Sparse Bayesian Learning

TL;DR

This paper introduces a novel Bayesian learning method for recovering block-sparse signals in MMV problems, effectively capturing structure and dependencies automatically, with competitive performance demonstrated through simulations.

Contribution

A new pattern-coupled hierarchical Gaussian prior model for automatic block-sparse signal recovery in MMV scenarios, utilizing an EM framework.

Findings

Effective recovery of block-sparse signals demonstrated

Automatic detection of sparsity patterns achieved

Competitive performance in simulations

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.