Message Passing Based Gaussian Mixture Model for DOA Estimation in Complex Noise Scenarios

Wireless signals are frequently disturbed by complex noise sources, presenting a challenge to traditional direction of arrival (DOA) estimation methods that rely on the assumption of Gaussian noise. To address this issue, this letter proposes an innovative Bayesian DOA estimation approach. This method utilizes Gaussian mixture model (GMM) and Dirichlet process prior to model the density function of complex noise in practical scenarios. Additionally, an efficient combined message passing algorithm is formulated on the factor graph through the use of generalized approximate message passing (GAMP) and mean field (MF) techniques. Simulation results validate the effectiveness of this algorithm.