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Article type: Research Article
Authors: Zheng, Yana | Li, Yanqia | Li, Weib | Yu, Leic; *
Affiliations: [a] Harbin Engineering University, Harbin, Heilongjiang, China | [b] Soochow University, Suzhou, Jiangsu, China | [c] Wuhan University, Wuhan, Hubei, China
Correspondence: [*] Corresponding author: Yu Lei, Wuhan University, Wuhan, Hubei, China. E-mail: 380464381@qq.com.
Abstract: In this paper, we present a new method for DOA estimation of the admix sources, which is named as Sparse Bayesian Learning for Low-rank and Sparse recovery (SBL-LSR). Considering the low-rank property of the stationary source and the sparsity property of the moving source over the multiple snapshots, SBL-LSR transforms DOA estimation of admix sources over all snapshots into recovering low-rank and sparse matrix from observation matrix. SBL-LSR is developed in the framework of sparse Bayesian learning, which provide the presetting piror of the parameter to be estimated. According to numerical simulations, SBL-LSR shows a superior performance on estimating admix sources and maintains high precision even under noisy perturbation.
Keywords: Direction-of-arrival, compressive robust principal component analysis, sparse Bayesian learning for low-rank and sparse recovery
DOI: 10.3233/JCM-180855
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 19, no. 2, pp. 407-416, 2019
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