Two-Dimensional Robust Source Localization Under Non-Gaussian Noise

Abstract

Various methods have been proposed to estimate the direction of arrival (DOA) of sources under the assumption of Gaussian noise. This assumption, based on the central limit theorem, has been mainly used because it offers an appropriate model in a homogeneous environment. Nevertheless, under certain conditions, the Gaussian hypothesis cannot fully represent the noisy environment. Consequently, these classical estimation methods are no longer suitable, and the use of non-Gaussian noise model is necessary. In this paper, we therefore treat the DOA estimation problem in a non-Gaussian framework based on the maximum likelihood approach using a compound-Gaussian (CG) noise model. We then propose the use of the expectation maximization (EM) algorithm to reduce the computational cost of the proposed algorithm. Several simulations are carried out to illustrate the interest of our approach compared to the state of the art.