Computationally efficient maximum likelihood estimation of structured covariance matrices
Computationally efficient maximum likelihood estimation of structured covariance matrices
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DOI：
10.1109/78.757219
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发表时间：
1999-05
期刊：
影响因子：
--
通讯作者：
Hongbin Li;P. Stoica;Jian Li
中科院分区：
文献类型：
--
作者：
Hongbin Li;P. Stoica;Jian Li
By invoking the extended invariance principle (EXIP), we present herein a computationally efficient method that provides asymptotic (for large samples) maximum likelihood (AML) estimation for structured covariance matrices and is referred to as the AML algorithm. A closed-form formula for estimating the Hermitian Toeplitz covariance matrices that makes AML computationally simpler than most existing Hermitian Toeplitz matrix estimation algorithms is derived. Although the AML covariance matrix estimator can be used in a variety of applications, we focus on array processing. Our simulation study shows that AML enhances the performance of angle estimation algorithms, such as MUSIC, by making them very close to the corresponding Cramer-Rao bound (CRB) for uncorrelated signals. Numerical comparisons with several structured and unstructured covariance matrix estimators are also presented.
