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Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning
Joint Authors
Liu, Zhang-Meng
Liu, Zheng
Feng, Dao-Wang
Huang, Zhi-Tao
Source
International Journal of Antennas and Propagation
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-27
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Liu, Zhang-Meng& Liu, Zheng& Feng, Dao-Wang& Huang, Zhi-Tao. 2014. Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning. International Journal of Antennas and Propagation،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1036282
Modern Language Association (MLA)
Liu, Zhang-Meng…[et al.]. Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning. International Journal of Antennas and Propagation No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1036282
American Medical Association (AMA)
Liu, Zhang-Meng& Liu, Zheng& Feng, Dao-Wang& Huang, Zhi-Tao. Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning. International Journal of Antennas and Propagation. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1036282
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1036282