Backtracking-Based Matching Pursuit Method for Sparse Signal Reconstruction

This letter presents a variant of Orthogonal Matching Pursuit (OMP) method, called Backtracking-based Adaptive OMP (BAOMP), for compressive sensing and sparse signal reconstruction. As an extension of the OMP algorithm, the BAOMP method incorporates a simple backtracking technique to detect the previous chosen atoms´ reliability and then deletes the unreliable atoms at each iteration. Through this modification, the BAOMP method achieves superior performance while maintaining the low complexity of OMP-type methods. Also, unlike its several predecessors, the BAOMP method does not require the sparsity level to be known a priori. The experiments demonstrate the proposed method´s superior performance to that of several other OMP-type and l₁ optimization methods.