An SBL-Based Coherent Source Localization Method Using Virtual Array Output

Zeyun ZHANG  Xiaohuan WU  Chunguo LI  Wei-Ping ZHU  

IEICE TRANSACTIONS on Communications   Vol.E102-B   No.11   pp.2151-2158
Publication Date: 2019/11/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2018EBP3309
Type of Manuscript: PAPER
Category: Antennas and Propagation
direction of arrival (DOA) estimation,  off-grid model,  sparse Bayesian learning (SBL),  sparse signal recovery (SSR),  coherent source,  

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Direction of arrival (DOA) estimation as a fundamental issue in array signal processing has been extensively studied for many applications in military and civilian fields. Many DOA estimation algorithms have been developed for different application scenarios such as low signal-to-noise ratio (SNR), limited snapshots, etc. However, there are still some practical problems that make DOA estimation very difficult. One of them is the correlation between sources. In this paper, we develop a sparsity-based method to estimate the DOA of coherent signals with sparse linear array (SLA). We adopt the off-grid signal model and solve the DOA estimation problem in the sparse Bayesian learning (SBL) framework. By considering the SLA as a ‘missing sensor’ ULA, our proposed method treats the output of the SLA as a partial output of the corresponding virtual uniform linear array (ULA) to make full use of the expanded aperture character of the SLA. Then we employ the expectation-maximization (EM) method to update the hyper-parameters and the output of the virtual ULA in an iterative manner. Numerical results demonstrate that the proposed method has a better performance in correlated signal scenarios than the reference methods in comparison, confirming the advantage of exploiting the extended aperture feature of the SLA.