Admix sources DOA estimation based on sparse Bayesian learning

作者：Zheng, Yan; Li, Yanqi; Li, Wei; Yu, Lei^*

来源：Journal of Computational Methods in Sciences and Engineering, 2019, 19(2): 407-416.

DOI：10.3233/JCM-180855

• Direction-of-arrival
• compressive robust principal component analysis
• sparse Bayesian learning for low-rank and sparse recovery

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.

• 出版日期2019
• 单位苏州大学; 武汉大学; 哈尔滨工程大学