Paper Publications

DOI number:10.3390/s151026267

Journal:Sensors

Key Words:Sparse Bayesian Learning (SBL); Direction-of-Arrival (DOA); Uniform Linear Array (ULA); mutual coupling

Abstract:Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs). Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD) to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise.

Co-author:Hu Nan,徐维超,常春起

First Author:戴继生

Indexed by:Journal paper

Volume:15

Issue:10

Page Number:26267-26280

Translation or Not:no

Date of Publication:2015-10-16

Included Journals:SCI