Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation

TL;DR

This paper extends block sparse Bayesian learning algorithms to better recover block sparse signals by exploiting intra-block correlation and accommodating unknown block structures, leading to improved recovery performance.

Contribution

It introduces two new algorithm families that leverage intra-block correlation and handle unknown block structures, enhancing signal recovery capabilities.

Findings

Exploiting intra-block correlation improves recovery accuracy.

Algorithms perform well even with unknown block structures.

Modified algorithms outperform existing methods in experiments.

Abstract

We examine the recovery of block sparse signals and extend the framework in two important directions; one by exploiting signals' intra-block correlation and the other by generalizing signals' block structure. We propose two families of algorithms based on the framework of block sparse Bayesian learning (BSBL). One family, directly derived from the BSBL framework, requires knowledge of the block structure. Another family, derived from an expanded BSBL framework, is based on a weaker assumption on the block structure, and can be used when the block structure is completely unknown. Using these algorithms we show that exploiting intra-block correlation is very helpful in improving recovery performance. These algorithms also shed light on how to modify existing algorithms or design new ones to exploit such correlation and improve performance.