Performance analysis of adaptive eigenanalysis algorithms

Author

Solo, Victor ; Kong, Xuan

Author_Institution

Dept. of Stat., Macquarie Univ., North Ryde, NSW, Australia

Volume

46

Issue

3

fYear

1998

fDate

3/1/1998 12:00:00 AM

Firstpage

636

Lastpage

646

Abstract

We present a rigorous analysis of several popular forms of short memory adaptive eigenanalysis algorithms using a stochastic averaging method. A first-order analysis shows that the algorithms do not have any equilibrium points despite published claims to the contrary. Through averaging analysis, we show that they hover around an appropriate eigenvector. A second-order analysis is also given without the Gaussian noise assumption, and our results greatly outperform an earlier approximation in the literature. The second-order analysis has been of much interest in the offline study but, in the dynamic adaptive case, is uncommon

Keywords

Gaussian noise; adaptive estimation; adaptive signal processing; eigenvalues and eigenfunctions; frequency estimation; stochastic processes; white noise; approximation; averaging analysis; eigenvector; first-order analysis; performance analysis; second-order analysis; short memory adaptive eigenanalysis algorithms; sinusoid signal frequency estimation; stochastic averaging method; white noise; Algorithm design and analysis; Eigenvalues and eigenfunctions; Filtering theory; Frequency estimation; Least squares approximation; Nonlinear filters; Performance analysis; Polynomials; Signal processing algorithms; Vectors;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.661331

Filename

661331