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

被引：7

作者：

Baktash, Ebrahim ^{[1
]}

Karimi, Mahmood ^{[1
]}

Wang, Xiaodong ^{[2
]}

机构：

[1] Shiraz Univ, Sch Elect & Comp Engn, Shiraz 7134851154, Iran

[2] Columbia Univ, Elect Engn Dept, New York, NY 10027 USA

来源：

IEEE COMMUNICATIONS LETTERS | 2019年 / 23卷 / 06期

关键词：

CES noise; EM method; DOA estimation;

D O I：

10.1109/LCOMM.2019.2911518

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

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, maximumlikelihood (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.

引用

页码：1041 / 1044

页数：4

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