Computationally efficient maximum likelihood estimation of structured covariance matrices

Hongbin Li, Petre Stoica, Jian Li

Research output: Contribution to journalArticlepeer-review

142 Scopus citations

Abstract

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 will be referred to as the AML algorithm. A closed-form formula for estimating 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 in this paper. Our simulation study shows that AML enhances the performances 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.

Original languageEnglish
Pages (from-to)1314-1323
Number of pages10
JournalIEEE Transactions on Signal Processing
Volume47
Issue number5
DOIs
StatePublished - 1999

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