Compressive sensing of block-sparse signals recovery based on sparsity adaptive regularized orthogonal matching pursuit algorithm

The structure of block sparsity in multi-band signals is prevalent. Among the block-sparse signal problems for compressive sensing, the most presenting recovery algorithms require block sparsity as prior information, whereas it may not be available in many practical applications. In this paper, a block sparsity adaptive regularized orthogonal matching pursuit algorithm (BAROMP) for compressive sensing is presented. The proposed algorithm could guarantee the accuracy of recovery by both the adaptive process which chooses the candidate set automatically and the regularization process which decides the atoms in the final support set. The simulation results show that the recovery probability of BAROMP which does not require sparsity as prior information is near to BROMP.