A sharp recovery condition for block sparse signals by block orthogonal multi-matching pursuit

TL;DR

This paper establishes a precise recovery condition for block sparse signals using the BOMMP algorithm, demonstrating improved performance by leveraging block structure in both noiseless and noisy scenarios.

Contribution

The paper provides a sharp bound based on block-RIC for exact recovery with BOMMP and shows that considering block structure enhances recovery success.

Findings

Sharp bound for exact recovery in noiseless case

Support recovery condition in noisy case

Block structure improves recovery performance

Abstract

We consider the block orthogonal multi-matching pursuit (BOMMP) algorithm for the recovery of block sparse signals. A sharp bound is obtained for the exact reconstruction of block $K$-sparse signals via the BOMMP algorithm in the noiseless case, based on the block restricted isometry constant (block-RIC). Moreover, we show that the sharp bound combining with an extra condition on the minimum $\ell_2$ norm of nonzero blocks of block $K-$sparse signals is sufficient to recover the true support of block $K$-sparse signals by the BOMMP in the noise case. The significance of the results we obtain in this paper lies in the fact that making explicit use of block sparsity of block sparse signals can achieve better recovery performance than ignoring the additional structure in the problem as being in the conventional sense.