Open AccessDOA Estimation Using Block Variational Sparse Bayesian Learning
Author(s) -
Huang Qinghua,
Zhang Guangfei,
Fang Yong
Publication year - 2017
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.04.004
Subject(s) - block (permutation group theory) , bayesian probability , estimation , computer science , artificial intelligence , bayes estimator , mathematics , bayesian inference , algorithm , pattern recognition (psychology) , combinatorics , management , economics
In Direction‐of‐arrival (DOA) estimation, the real‐valued sparse Bayesian algorithm degrades the estimation performance by decomposing the complex value into real and imaginary components and combining them independently. We directly use complex probability density functions to model the noise and complex‐valued sparse direction weights. Based on the Multiple measurement vectors (MMV), block sparse structure for the direction weights is integrated into the variational Bayesian learning to provide accurate source direction estimates. The proposed algorithm can be used for arbitrary array geometries and does not need the prior information of the incident signal number. Simulation results demonstrate the better performance of the proposed method compared with the real‐valued sparse Bayesian algorithm, the Orthogonal matching pursuit (OMP) and l 1 norm based complex‐valued methods.