Extended Chandrasekhar recursions

Author

Sayed, Ali H. ; Kailath, Thomas

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA

Volume

39

Issue

3

fYear

1994

fDate

3/1/1994 12:00:00 AM

Firstpage

619

Lastpage

623

Abstract

We extend the discrete-time Chandrasekhar recursions for least-squares estimation in constant parameter state-space models to a class of structured time-variant state-space models, special cases of which often arise in adaptive filtering. It can be shown that the much studied exponentially weighted recursive least-squares filtering problem can be reformulated as an estimation problem for a state-space model having this special time-variant structure. Other applications arise in the multichannel and multidimensional adaptive filtering context

Keywords

Kalman filters; adaptive filters; least squares approximations; matrix algebra; parameter estimation; state-space methods; constant parameter state-space models; exponentially weighted recursive least-squares filtering; extended discrete-time Chandrasekhar recursions; least-squares estimation; multichannel adaptive filtering; multidimensional adaptive filtering; structured time-variant state-space models; Adaptive filters; Filtering algorithms; Information systems; Kalman filters; Multidimensional systems; Parameter estimation; Recursive estimation; Resonance light scattering; Riccati equations; State estimation;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.280773

Filename

280773