Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning

Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning

A spatial filtering-based relevance vector machine (RVM) is proposed in this paper to separate coherent sources and estimate their directions-of-arrival (DOA), with the filter parameters and DOA estimates initialized and refined via sparse Bayesian learning. The RVM is used to exploit the spatial sp...

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Main Authors: Zhang-Meng Liu, Zheng Liu, Dao-Wang Feng, Zhi-Tao Huang
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:International Journal of Antennas and Propagation
Online Access:http://dx.doi.org/10.1155/2014/959386
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spelling doaj-78dde94d97ac414cba075c2e09a471d72020-11-24T21:06:31ZengHindawi LimitedInternational Journal of Antennas and Propagation1687-58691687-58772014-01-01201410.1155/2014/959386959386Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian LearningZhang-Meng Liu0Zheng Liu1Dao-Wang Feng2Zhi-Tao Huang3The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, ChinaCollege of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaThe State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, ChinaA spatial filtering-based relevance vector machine (RVM) is proposed in this paper to separate coherent sources and estimate their directions-of-arrival (DOA), with the filter parameters and DOA estimates initialized and refined via sparse Bayesian learning. The RVM is used to exploit the spatial sparsity of the incident signals and gain improved adaptability to much demanding scenarios, such as low signal-to-noise ratio (SNR), limited snapshots, and spatially adjacent sources, and the spatial filters are introduced to enhance global convergence of the original RVM in the case of coherent sources. The proposed method adapts to arbitrary array geometry, and simulation results show that it surpasses the existing methods in DOA estimation performance.http://dx.doi.org/10.1155/2014/959386
collection DOAJ
language English
format Article
sources DOAJ
author Zhang-Meng Liu
Zheng Liu
Dao-Wang Feng
Zhi-Tao Huang
spellingShingle Zhang-Meng Liu
Zheng Liu
Dao-Wang Feng
Zhi-Tao Huang
Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning
International Journal of Antennas and Propagation
author_facet Zhang-Meng Liu
Zheng Liu
Dao-Wang Feng
Zhi-Tao Huang
author_sort Zhang-Meng Liu
title Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning
title_short Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning
title_full Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning
title_fullStr Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning
title_full_unstemmed Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning
title_sort direction-of-arrival estimation for coherent sources via sparse bayesian learning
publisher Hindawi Limited
series International Journal of Antennas and Propagation
issn 1687-5869
1687-5877
publishDate 2014-01-01
description A spatial filtering-based relevance vector machine (RVM) is proposed in this paper to separate coherent sources and estimate their directions-of-arrival (DOA), with the filter parameters and DOA estimates initialized and refined via sparse Bayesian learning. The RVM is used to exploit the spatial sparsity of the incident signals and gain improved adaptability to much demanding scenarios, such as low signal-to-noise ratio (SNR), limited snapshots, and spatially adjacent sources, and the spatial filters are introduced to enhance global convergence of the original RVM in the case of coherent sources. The proposed method adapts to arbitrary array geometry, and simulation results show that it surpasses the existing methods in DOA estimation performance.
url http://dx.doi.org/10.1155/2014/959386
work_keys_str_mv AT zhangmengliu directionofarrivalestimationforcoherentsourcesviasparsebayesianlearning
AT zhengliu directionofarrivalestimationforcoherentsourcesviasparsebayesianlearning
AT daowangfeng directionofarrivalestimationforcoherentsourcesviasparsebayesianlearning
AT zhitaohuang directionofarrivalestimationforcoherentsourcesviasparsebayesianlearning
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