On adaptive EVD asymptotic distribution of centro-symmetric covariance matrices

Author

Delmas, Jean-Pierre

Author_Institution

Inst. Nat. des Telecommun., Evry, France

Volume

47

Issue

5

fYear

1999

fDate

5/1/1999 12:00:00 AM

Firstpage

1402

Lastpage

1406

Abstract

This article investigates the gain in statistical performance/complexity of the adaptive estimation of the eigenvalue decomposition (EVD) of covariance matrices when the centro-symmetric (CS) structure of such matrices is utilized. After deriving the asymptotic distribution of the EVD estimators, it is shown, in particular, that the closed-form expressions for the asymptotic covariance of batch and adaptive EVD estimators are very similar, provided that the number of samples is replaced by the inverse of the step size

Keywords

adaptive estimation; adaptive signal processing; computational complexity; covariance matrices; eigenvalues and eigenfunctions; matrix decomposition; statistical analysis; adaptive EVD asymptotic distribution; adaptive EVD estimators; adaptive estimation; asymptotic covariance; batch EVD estimators; centro-symmetric covariance matrices; closed-form expressions; covariance matrices; eigenvalue decomposition; signal processing; statistical performance/complexity; step size inverse; Adaptive filters; Adaptive signal processing; Blind equalizers; Covariance matrix; Finite impulse response filter; Performance gain; Signal processing; Signal processing algorithms; Symmetric matrices; Wiener filter;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.757231

Filename

757231