Skip to Main Content
Direction-of-arrival (DOA) estimation for wideband source signals using near-field acoustic sensor networks has been drawing a lot of research interest recently. A wide variety of DOA estimation approaches are based on the predominant maximum-likelihood objective. In this paper, we would like to tackle with the DOA estimation problem based on the realistic assumption where the sources are corrupted by spatially non-white noises. We explore the respective limitations of two popular DOA methods for solving this problem, namely the SC-ML and AC-ML algorithms, and design a new expectation maximization (EM) algorithm. Through Monte Carlo simulations, it is demonstrated that our proposed EM algorithm outperforms the SC-ML and AC-ML methods in terms of the DOA accuracy.