Multiwindow estimators of correlation

Author

McWhorter, L. Todd ; Scharf, Louis L.

Author_Institution

Dept. of Electr. & Comput. Eng., Colorado Univ., Boulder, CO, USA

Volume

46

Issue

2

fYear

1998

fDate

2/1/1998 12:00:00 AM

Firstpage

440

Lastpage

448

Abstract

This paper is concerned with the structure of estimators of correlation matrices and correlation sequences. We argue that reasonable estimators of the correlation matrix are quadratic in the data and nonnegative definite. We also specify the structure of the estimator when the data are modulated: a property we call modulation covariance. We state a representation theorem for estimators that have these attributes. We also derive a representation for estimators that have the additional requirement that the estimated matrix be Toeplitz. These representation theorems admit estimators that use multiwindowed copies of the data. In many circumstances, this multiwindow structure is superior to the conventional sum of lagged-products or outer-product estimators

Keywords

Toeplitz matrices; correlation methods; modulation; parameter estimation; sequences; spectral analysis; Toeplitz matrix; correlation matrices; correlation sequences; mean square error bound; modulated data; modulation covariance; multiwindow estimators; outer-product estimators; representation theorems; spectral analysis; sum of lagged-products; Array signal processing; Constraint theory; Covariance matrix; Detectors; Estimation theory; Frequency estimation; Parameter estimation; State estimation; Time measurement; Vectors;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.655428

Filename

655428