Sparse Bayesian learning for DOA estimation with mutual coupling.
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.
School of Electrical and Information Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China. jsdai@ujs.edu.cn.|National Mobile Communications Research Laboratory, Southeast University, 2 Sipailou Road, Nanjing 210096, China. jsdai@ujs.edu.cn.
Jisheng
J
Dai
School of Electronic and Information Engineering, Soochow University, 178 East Ganjiang Road, Suzhou 215006, China. hunan@suda.edu.cn.
Nan
N
Hu
Department of Automatic Control, Guangdong University of Technology, 100 Huanxi Road, Guangzhou 510006, China. wcxu@gdut.edu.cn.
Weichao
W
Xu
School of Electronic and Information Engineering, Soochow University, 178 East Ganjiang Road, Suzhou 215006, China. cqchang@suda.edu.cn.
Chunqi
C
Chang
Direction-of-Arrival (DOA); Sparse Bayesian Learning (SBL); Uniform Linear Array (ULA); mutual coupling