Computationally efficient maximum-likelihood estimation of structured covariance matrices

Hongbin Li; Petre Stoica; Jian Li

doi:10.1109/ICASSP.1998.681615

Computationally efficient maximum-likelihood estimation of structured covariance matrices

Hongbin Li, Petre Stoica, Jian Li

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998
Pages	2325-2328
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.1998.681615
State	Published - 1998
Event	1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998 - Seattle, WA, United States Duration: 12 May 1998 → 15 May 1998

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	4
ISSN (Print)	1520-6149

Conference

Conference	1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998
Country/Territory	United States
City	Seattle, WA
Period	12/05/98 → 15/05/98

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10.1109/ICASSP.1998.681615

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Li, H., Stoica, P., & Li, J. (1998). Computationally efficient maximum-likelihood estimation of structured covariance matrices. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998 (pp. 2325-2328). Article 681615 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 4). https://doi.org/10.1109/ICASSP.1998.681615

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title = "Computationally efficient maximum-likelihood estimation of structured covariance matrices",

abstract = "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.",

author = "Hongbin Li and Petre Stoica and Jian Li",

year = "1998",

doi = "10.1109/ICASSP.1998.681615",

language = "English",

isbn = "0780344286",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

pages = "2325--2328",

booktitle = "Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998",

note = "1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998 ; Conference date: 12-05-1998 Through 15-05-1998",

}

Li, H, Stoica, P & Li, J 1998, Computationally efficient maximum-likelihood estimation of structured covariance matrices. in Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998., 681615, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 4, pp. 2325-2328, 1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998, Seattle, WA, United States, 12/05/98. https://doi.org/10.1109/ICASSP.1998.681615

Computationally efficient maximum-likelihood estimation of structured covariance matrices. / Li, Hongbin; Stoica, Petre; Li, Jian.
Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998. 1998. p. 2325-2328 681615 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

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Li H, Stoica P, Li J. Computationally efficient maximum-likelihood estimation of structured covariance matrices. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998. 1998. p. 2325-2328. 681615. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.1998.681615

Computationally efficient maximum-likelihood estimation of structured covariance matrices

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