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

Liu, Zhang-Meng ; Liu, Zheng ; Feng, Dao-Wang ; Huang, Zhi-Tao Pastorino, Matteo

International journal of antennas and propagation, 2014-01, Vol.2014, p.1-8 [Periódico revisado por pares]

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Título:
Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning
Autor: Liu, Zhang-Meng ; Liu, Zheng ; Feng, Dao-Wang ; Huang, Zhi-Tao
Pastorino, Matteo
Assuntos: Antennas ; Arrays ; Bayesian analysis ; Coherence ; Convergence ; Economic models ; Estimates ; Information systems ; Learning ; Mathematical analysis ; Methods ; Sparsity
É parte de: International journal of antennas and propagation, 2014-01, Vol.2014, p.1-8
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: 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.
Editor: New York: Hindawi Publishing Corporation
Idioma: Inglês

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