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International Journal of Antennas and Propagation
Volume 2014, Article ID 959386, 8 pages
Research Article

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

1The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, China
2College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China

Received 26 October 2013; Accepted 24 March 2014; Published 27 April 2014

Academic Editor: Matteo Pastorino

Copyright © 2014 Zhang-Meng Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.