Comparison and Simulation Study of the Sparse Representation Matching Pursuit Algorithm and the Orthogonal Matching Pursuit Algorithm

In recent years, sparse representation technology has made outstanding contributions in signal processing, image processing, target recognition, blind source separation, etc. Greedy algorithms are an important class of algorithms in the field of sparse representation, and matching pursuit algorithm and orthogonal matching pursuit algorithm are two typical greedy algorithms. The orthogonal matching pursuit algorithm has a great improvement in the convergence time compared with the matching pursuit algorithm, but the matching pursuit algorithm is still used in many current scenarios where greedy algorithm is applied. In order to understand the signal reconstruction effect of greedy algorithms and to analyze and compare the advantages and disadvantages of different algorithms, this paper briefly analyzes and compares these two typical greedy algorithms from the principle to simulation, and obtains the simulation results of matching pursuit algorithm and orthogonal matching pursuit algorithm and compares the advantages and disadvantages.