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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Published in | International journal of antennas and propagation Vol. 2014; no. 2014; pp. 1 - 8 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Cairo, Egypt
Hindawi Publishing Corporation
01.01.2014
Hindawi Limited |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1687-5869 1687-5877 |
DOI: | 10.1155/2014/959386 |