DOI码：10.3390/s151026267

发表刊物：Sensors

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

摘要：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.

合写作者：Hu Nan,徐维超,常春起

第一作者：戴继生

论文类型：期刊论文

卷号：15

期号：10

页面范围：26267-26280

是否译文：否

发表时间：2015-10-16

收录刊物：SCI