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Maximum-Likelihood Direction Finding Under Elliptical Noise Using the EM Algorithm
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DOI：10.1109/LCOMM.2019.2911518.png)
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摘要
En 中文
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.
Keyword:
CES noise
EM method
DOA estimation
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期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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