Admix sources DOA estimation based on sparse Bayesian learning
Yan Zheng, Yanqi Li, Wei Li, Lei Yu · Journal of Computational Methods in Sciences and Engineering · 2018
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 snapshot s, 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.