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Computationally efficient maximum-likelihood estimation of structured covariance matrices

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3 Author(s)
Hongbin Li ; Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL, USA ; P. Stoica ; Jian Li

A computationally efficient method for structured covariance matrix estimation is presented. The proposed method provides an asymptotic (for large samples) maximum likelihood estimate of a structured covariance matrix and is referred to as AML. 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 and show that AML enhances the performance of angle estimation algorithms, such as MUSIC, by making them attain the corresponding Cramer-Rao bound (CRB) for uncorrelated signals

Published in:

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on  (Volume:4 )

Date of Conference:

12-15 May 1998