Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation
This work addresses signal recovery problems in fields like communications or imaging, but it is incremental as it builds upon existing BSBL methods.
The authors tackled the recovery of block sparse signals by extending block sparse Bayesian learning (BSBL) algorithms to exploit intra-block correlation and generalize block structures, resulting in improved recovery performance as demonstrated in their experiments.
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.