A new family of EVD tracking algorithms using Givens rotations

Author

Champagne, Benoît ; Liu, Qing-Guang

Author_Institution

INRS-Telecommun., Quebec Univ., Verdun, Que., Canada

Volume

5

fYear

1996

fDate

7-10 May 1996

Firstpage

2539

Abstract

In this work, we derive new algorithms for tracking the eigenvalue decomposition (EVD) of a time-varying data covariance matrix. These algorithms have parallel structures, low operation counts and good convergence behavior. Their main feature is the use of Givens rotations to update the eigenvector estimates. As a result, orthonormality of the latter can be maintained at all time, which is critical in the application of certain signal-subspace methods. The comparative performance of the new algorithms is illustrated by means of computer experiments

Keywords

convergence of numerical methods; covariance matrices; eigenvalues and eigenfunctions; parallel algorithms; signal processing; time-varying systems; tracking; EVD tracking algorithms; Givens rotations; convergence behavior; eigenvalue decomposition; eigenvector estimates; operation counts; parallel structures; signal-subspace methods; time-varying data covariance matrix; Application software; Business; Convergence of numerical methods; Councils; Covariance matrix; Eigenvalues and eigenfunctions; Matrix decomposition; Numerical stability; Performance evaluation; Recursive estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on

Conference_Location

Atlanta, GA

ISSN

1520-6149

Print_ISBN

0-7803-3192-3

Type

conf

DOI

10.1109/ICASSP.1996.547981

Filename

547981