Computationally efficient maximum likelihood estimation of structured covariance matrices

Hongbin Li, Petre Stoica, Jian Li

Research output: Contribution to journalArticlepeer-review

Abstract

By invoking the extended invariance principle (EXIP), we present herein a computationally efficient method that provides Asymptotic (for large samples) Maximum Likelihood (AML) es- timation for structured covariance matrices and will be referred to as the AML algorithm. A closed-form formula for estimating Hermitian Toeplitz covariance matrices is derived which makes AML computationally much simpler than most existing Hermitian Toeplitz matrix estimation algorithms. The AML covariance matrix estimator can be used in a variety of applications. We focus on array processing herein and show that AML enhances the performance of angle estima- tion algorithms, such as MUSIC, by making them attain the corresponding CramerRao bound (CRB) for uncorrelated signals. Numerical comparisons with several structured and unstructured covariance matrix estimators are also presented.

Original languageEnglish
Pages (from-to)823
Number of pages1
JournalIEEE Transactions on Signal Processing
Volume46
Issue number3
StatePublished - 1998

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