Fast orthonormal PAST algorithm

Author

Abed-Meraim, K. ; Chkeif, A. ; Hua, Y.

Author_Institution

Telecom Paris, France

Volume

7

Issue

3

fYear

2000

fDate

3/1/2000 12:00:00 AM

Firstpage

60

Lastpage

62

Abstract

Subspace decomposition has proven to be an important tool in adaptive signal processing. A number of algorithms have been proposed for tracking the dominant subspace. Among the most robust and most efficient methods is the projection approximation and subspace tracking (PAST) method. This paper elaborates on an orthonormal version of the PAST algorithm for fast estimation and tracking of the principal subspace or/and principal components of a vector sequence. The orthonormal PAST (OPAST) algorithm guarantees the orthonormality of the weight matrix at each iteration. Moreover, it has a linear complexity like the PAST algorithm and a global convergence property like the natural power (NP) method.

Keywords

adaptive signal processing; approximation theory; computational complexity; convergence of numerical methods; matrix algebra; parameter estimation; tracking; OPAST algorithm; adaptive signal processing; fast estimation; fast orthonormal PAST algorithm; fast tracking; global convergence property; iteration; linear complexity; natural power method; principal components; principal subspace components; projection approximation and subspace tracking; vector sequence; weight matrix; Adaptive signal processing; Approximation algorithms; Convergence; Covariance matrix; Data compression; Matrix decomposition; Principal component analysis; Signal processing algorithms; System identification; Vectors;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/97.823526

Filename

823526