Fast adaptive eigenvalue decomposition: a maximum likelihood approach

Author

Riou, Christian ; Chonavel, Thierry

Author_Institution

SC Dept., ENSTB, Brest, France

Volume

5

fYear

1997

fDate

21-24 Apr 1997

Firstpage

3565

Abstract

A new adaptive subspace estimation algorithm is presented, based on the maximisation of the likelihood functional. It requires little computational cost and the particular structure of the algorithm ensures the orthonormality of the estimated basis of eigenvectors. Application to moving sources localization shows the very good behavior of the algorithm when applied to problems of practical interest

Keywords

adaptive estimation; adaptive signal processing; convergence of numerical methods; covariance matrices; eigenvalues and eigenfunctions; matrix decomposition; maximum likelihood estimation; adaptive eigenvalue decomposition; adaptive subspace estimation algorithm; convergence properties; covariance matrix; eigenvectors; fast algorithm; maximum likelihood approach; moving sources localization; orthonormality; signal processing; Computational efficiency; Constraint optimization; Costs; Covariance matrix; Direction of arrival estimation; Eigenvalues and eigenfunctions; Frequency estimation; Maximum likelihood estimation; Signal processing algorithms; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on

Conference_Location

Munich

ISSN

1520-6149

Print_ISBN

0-8186-7919-0

Type

conf

DOI

10.1109/ICASSP.1997.604636

Filename

604636