Maximum-Likelihood Direction Finding Under Elliptical Noise Using the EM Algorithm

Unlike subspace-based solutions of direction-of-arrival (DOA) estimation under non-Gaussian noise, where the only optional difference with the Gaussian case is the scatter/covariance matrix estimation method, maximum-likelihood (ML)-based DOA solutions need a different treatment under the non-Gaussi...

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Bibliographic Details
Published inIEEE communications letters Vol. 23; no. 6; pp. 1041 - 1044
Main Authors Baktash, Ebrahim, Karimi, Mahmood, Wang, Xiaodong
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Arrays
CES noise
Covariance matrix
Direction finding
Direction of arrival
Direction-of-arrival estimation
DOA estimation
Economic models
EM method
Generators
Maximum likelihood estimation
Normal distribution
Probability density function
Random noise
Symmetric matrices
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ISSN1089-7798
1558-2558
DOI10.1109/LCOMM.2019.2911518

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Summary:Unlike subspace-based solutions of direction-of-arrival (DOA) estimation under non-Gaussian noise, where the only optional difference with the Gaussian case is the scatter/covariance matrix estimation method, maximum-likelihood (ML)-based DOA solutions need a different treatment under the non-Gaussianity assumption. In this letter, we derive a particular ML-based DOA solution, called the expectation-maximization (EM) estimator, under the wide class of complex elliptically symmetric (CES) distributions.
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2019.2911518