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

被引：0
|
作者：
Guan, Shanwen [1 ]
Lu, Xinhua [2 ]
Li, Ji [1 ]
Luo, Xiaonan [1 ]
机构：
[1] Guilin Univ Elect Technol, Guangxi Key Lab Image & Graph Intelligent Proc, Guilin 541000, Peoples R China
[2] Nanyang Inst Technol, Sch Informat Engn, Nanyang 473000, Peoples R China
来源：
IEEE SIGNAL PROCESSING LETTERS | 2024年 / 31卷
基金：
中国国家自然科学基金;
关键词：
Direction of arrival (DOA) estimation; factor graph; message passing algorithm; Gaussian mixture model; LOCALIZATION; ALGORITHM;
D O I：
10.1109/LSP.2024.3386496
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：1379 / 1383
页数：5
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