  Remarkable properties of Compressed sensing (CS) has led researchers to
utilize it in various other fields where a solution to an underdetermined
system of linear equations is needed. One such application is in the area of
array signal processing e.g. in signal denoising and Direction of Arrival (DOA)
estimation. From the two prominent categories of CS recovery algorithms, namely
convex optimization algorithms and greedy sparse approximation algorithms, we
investigate the application of greedy sparse approximation algorithms to
estimate DOA in the uniform linear array (ULA) environment. We conduct an
empirical investigation into the behavior of the two state-of-the-art greedy
algorithms: OMP and CoSaMP. This investigation takes into account the various
scenarios such as varying degrees of noise level and coherency between the
sources. We perform simulations to demonstrate the performances of these
algorithms and give a brief analysis of the results.
