Admix sources DOA estimation based on sparse Bayesian learning

Article type: Research Article

Authors: Zheng, Yan^a | Li, Yanqi^a | Li, Wei^b | Yu, Lei^{c; *}

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: [email protected].

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