DOA Estimation using Random Linear Arrays via Compressive Sensing

In this article we analyze the performance of nonuniform linear arrays for Direction of Arrival (DOA) estimation. We use different classes of sparse recovery algorithms to estimate the direction of arrival of the signal sources and its information. We focus on three array configurations, a structured or virtual array with prefixed potential locations for the elements, a random array and a random array with a restriction on the minimum distance between elements. We provide simulations of the performance of each configuration under these algorithms for different values of aperture, and number of signal sources.