Joint Localization and Source Association Sparse Bayesian Learning Under Multipath Propagation

Abstract

This article focuses on the topic of joint direction of arrival (DOA), source association, and attenuation coefficient estimation under multipath environment. Most existing methods adopt the sequential three-phase estimation, resulting in the nuisance dependency between the estimation accuracy of the current phase and the previous phase. Besides, they also require some accurate prior information, including the accurate DOA initialization, and the number of incoherent sources and spatial paths, which is unrealistic in practice. To solve this problem, the joint localization and source association sparse Bayesian learning (JLSA-SBL) algorithm is proposed to integrate the source association process, DOA, and attenuation coefficient estimation into a unified parameter estimation framework. The proposed method exploits the underlying sparsity and coherent structure of the incident signals to achieve more accurate joint parameter estimation. Compared to the previous methods, JLSA-SBL can directly estimate the latent multipath propagation parameters even in the absence of prior information. Besides, the JLSA-SBL also has superior performance in distinguishing the closely spaced multipath signals belonging to different sources. Numerical simulation experiments have been performed to demonstrate the superior performance of the proposed method.