Open AccessHigh‐resolution DOA estimation for closely spaced correlated signals using unitary sparse Bayesian learning
Author(s) -
Lei Wenying,
Chen Baixiao
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.1317
Subject(s) - algorithm , matrix (chemical analysis) , computer science , hermitian matrix , sparse matrix , scheme (mathematics) , direction of arrival , bayesian probability , unitary matrix , bayesian inference , pattern recognition (psychology) , artificial intelligence , mathematics , unitary state , telecommunications , political science , law , mathematical analysis , materials science , physics , quantum mechanics , antenna (radio) , pure mathematics , composite material , gaussian
A novel method is proposed to effectively solve the challenging problem of direction‐of‐arrival (DOA) estimation for closely spaced correlated signals. A centro‐Hermitian extended matrix is exploited to double the number of data samples, and then is transformed into a real‐valued data matrix. An improved sparse Bayesian learning scheme is utilised to estimate DOAs by recovering the real‐valued jointly row‐sparse solution matrix with a reduced computational burden. The proposed method not only provides increased estimation accuracy but also has improved angular separation performance. Simulation results validate the effectiveness of the proposed method.