Sparse signal recovery with OMP algorithm using sensing measurement matrix

Orthogonal matching pursuit (OMP) algorithm with random measurement matrix (RMM), often selects an incorrect variable due to the induced coherent interference between the columns of RMM. In this paper, we propose a sensing measurement matrix (SMM)-OMP which mitigates the coherent interference and thus improves the successful recovery probability of signal. It is shown that the SMM-OMP selects all the significant variables of the sparse signal before selecting the incorrect ones. We present a mutual incoherent property (MIP) based theoretical analysis to verify that the proposed method has a better performance than RMM-OMP. Various simulation results confirm our proposed method efficiency.