Variational Bayesian Gaussian mixture model for off‐grid DOA estimation

• Published in: Electronics letters Vol. 60; no. 3
• Main Authors: Guan, Shanwen, Li, Ji, Luo, Xiaonan
• Format: Journal Article
• Language: English
• Published: Wiley 01.02.2024
• Subjects: Bayesian methods; noise; signal processing
• ISSN: 0013-5194; 1350-911X
• DOI: 10.1049/ell2.13114

Wireless signals are commonly subject to diverse and complex noise interference. The typical assumption of Gaussian white noise often oversimplifies the noise, resulting in reduced accuracy in estimating the direction of arrival (DOA), especially in complex scenarios. To tackle this issue, this paper introduces a new Bayesian model for off‐grid DOA estimation. This model utilizes Gaussian mixture model (GMM)‐based Dirichlet processes (DP) to characterize noise, allowing adaptive adjustments in the number of Gaussian mixture models. Leveraging the factor graph representation of the Bayesian model, a low‐complexity mixed messaging passing algorithm, employing generalized approximate message passing (GAMP) and mean field (MF), is proposed. Simulation results validate the efficacy of the proposed algorithm. This paper addresses limitations in estimating direction of arrival caused by Gaussian white noise in wireless signals. It introduces a novel Bayesian model for off‐grid DOA estimation, characterizing noise using Gaussian Mixture Model based Dirichlet processes that allow adaptive adjustments of GMM quantity. A low‐complexity mixed messaging passing algorithm based on factor graphs is proposed, employing GAMP and MF methods, with simulation results confirming the algorithm's effectiveness.