Adaptive threshold backtracking matching pursuit for compressive sensing

Compressive sensing (CS) is a sampling technique designed for reducing the complexity of sparse data acquisition. In this paper, a modified Orthogonal Matching Pursuit (OMP) method, called Adaptive Threshold Backtracking OMP (ATBOMP) for compressive sensing and sparse signal reconstruction is presented. Compared with the standard OMP algorithm, the ATBOMP method incorporates an adaptive threshold technique to choose candidate set and ensure the support set´s reliability by regularized procession. Through this modification, the ATBOMP method improves the reconstruction probability of the sparse signal and achieves superior performance. Also, the ATBOMP method does not require the sparsity level to be known as a priori. The experiments demonstrate the proposed method´s superior performance to that of several other OMP-type and l1 optimization methods.