Compressive Sensing (CS) is a new method of detection that has emerged in recent years. It allows the detection of sparse signals from under-determined systems. While the detection was originally defined for convex optimization, various variants of the greedy Matching Pursuit (MP) algorithm have been created to solve the problem more efficiently.

The goal of this thesis is to first get an overview of the variants of MP, that have defined in the literature (e.g. OMP, CoSaMP, StOMP, ROMP, PrOMP, LSOP, SOMP, and OLS), and compare how they differ from one another. Later these algorithms should be implemented in MATLAB and their performance compared for a few specific sample scenarios.