New insights in the analysis of polynomial adaptive filters

Author

Therrien, Charles W. ; Jenkins, W. Kenneth

Author_Institution

Dept. of Electr. & Comput. Eng., Naval Postgraduate Sch., Monterey, CA, USA

fYear

1996

Firstpage

382

Lastpage

385

Abstract

New results are reported on the structure of the correlation matrix for the data vector in Volterra second order adaptive filters for a general colored Gaussian input process. The structure becomes apparent when the input to the quadratic part of the filter is represented as a Kronecker product of the vector of terms to the linear part, and the redundant terms in the product are not removed. This approach leads to bounds on the eigenvalues of the correlation matrix which characterize the performance of LMS algorithms, and suggestions for possibly improved nonlinear adaptive filtering algorithms

Keywords

Gaussian processes; Volterra equations; adaptive filters; adaptive signal processing; correlation methods; eigenvalues and eigenfunctions; least mean squares methods; matrix algebra; nonlinear filters; polynomials; Kronecker product; LMS algorithms; Volterra second order adaptive filters; colored Gaussian input process; correlation matrix; data vector; eigenvalues bounds; nonlinear adaptive filtering algorithms; polynomial adaptive filters; redundant terms; Adaptive algorithm; Adaptive filters; Convergence; Eigenvalues and eigenfunctions; Equations; Filtering algorithms; Least squares approximation; Nonlinear filters; Polynomials; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing Workshop Proceedings, 1996., IEEE

Conference_Location

Loen

Print_ISBN

0-7803-3629-1

Type

conf

DOI

10.1109/DSPWS.1996.555541

Filename

555541