On sparse methods for array signal processing in the presence of interference

Abstract

We analyze the performance of several algorithms designed to solve the inverse sparse problem when they are applied to array signal processing. Specifically we study the error on the estimation of the complex envelope and the direction of arrival of signals of interest in the presence of interference sources using a uniform linear array. In particular, we compare the performance of the Enhanced Sparse Bayesian Learning (ESBL) algorithm against different algorithms tailored to this scenario. Since the former exploits interference information to diminish its unwanted effects, we find that it provides a reasonable tradeoff between runtime and estimation error.