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

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

2012-01-04

arxiv.org/abs/1201.0862v1

Statistics

Machine Learning

Scientific paper

We examine the recovery of block sparse signals and extend the framework in two important directions; one by exploiting intra-block correlation and the other by generalizing the 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 partition. Another family, derived from an expanded bSBL framework, is based on a weaker assumption about the a priori information of the block structure, and can be used in the cases when block partition, block size, block-sparsity are all unknown. Using these algorithms we show that exploiting intra-block correlation is very helpful to improve recovery performance. These algorithms also shed light on how to modify existing algorithms or design new ones to exploit such correlation for improved performance.

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