Paper Info
Title
Iterative Methods For Doa Estimation Of Correlated Sources In Spatially Colored Noise Fields
Abstract
Direction of arrival (DOA) estimation in the presence of correlated sources and unknown spatially colored noise field is concerned in this paper. We develop two iterative approaches to jointly estimate the angle parameters and unknown nonuniform noise covariance matrix from multi-snapshot sensor array data. Specifically, we show that both methods allow to obtain the estimates by explicit formulas: i) By concentrating the ML estimation problem with regard to all nuisance parameters, the proposed method provides a concise derivation of the concentrated likelihood function; ii) compact expressions of the estimates of the parameters of interest from sparse Bayesian learning (SBL) principle are also presented. This technique can be deemed as an alternative to ML estimation, and it provides better accuracy often at a lower computational cost. In spatially colored noise fields, the proposals are free of any further structural constraints that are usually imposed on the received signals in most existing direction finding approaches. Extensive simulations and experimental results are presented to demonstrate the satisfying performance of the proposed methods. (c) 2021 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.sigpro.2021.108100
SIGNAL PROCESSING
Keywords
DocType
Volume
Direction-of-arrival (DOA) estimation, Spatially colored noise, Maximum likelihood (ML), Sparse Bayesian learning (SBL)
Journal
185
ISSN
Citations 
PageRank 
0165-1684
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Jie Yang119457.20
Yixin Yang23311.80
Jieyi Lu300.34
Long Yang404.06