Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning. (27th April 2014)

• Record Type: Journal Article
• Authors: Liu, Zhang-Meng; Liu, Zheng; Feng, Dao-Wang; Huang, Zhi-Tao
• Other Names: Pastorino Matteo Academic Editor.
• Is Part Of: International journal of antennas and propagation. Volume 2014(2014)
• Publication Date: 2014-04-27
• Subjects: Electronic apparatus and appliances -- Periodicals; Antennas (Electronics) -- Periodicals; Radio wave propagation -- Periodicals; Antennes (Électronique); Ondes radioélectriques -- Propagation; Antennas (Electronics); Electronic apparatus and appliances; Radio wave propagation; Electronic journals; Periodicals; 621.382405
• DOI: 10.1155/2014/959386
• Languages: English
• ISSNs: 1687-5869

Abstract

Abstract : 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.