Computationally efficient algorithms for third order adaptive Volterra filters

Author

Li, Xiaohui ; Jenkins, W. Kenneth ; Therrien, Charles W.

Author_Institution

Dept. of Electr. & Comput. Eng., Illinois Univ., Urbana, IL, USA

Volume

3

fYear

1998

fDate

12-15 May 1998

Firstpage

1405

Abstract

The input autocorrelation matrix for a third order (cubic) Volterra adaptive filter for general colored Gaussian input processes is analyzed to determine how to best formulate a computationally efficient fast adaptive algorithm. When the input signal samples are ordered properly within the input data vector, the autocorrelation matrix of the cubic filter inherits a block diagonal structure, with some of the sub-blocks also having diagonal structure. A computationally efficient adaptive algorithm is presented that takes advantage of the sparsity and unique structure of the correlation matrix that results from this formulation

Keywords

Gaussian processes; Newton method; Volterra series; adaptive filters; adaptive signal processing; conjugate gradient methods; correlation methods; filtering theory; matrix algebra; signal sampling; block diagonal structure; computationally efficient algorithms; conjugate gradient algorithm; cubic filter; fast adaptive algorithm; general colored Gaussian input processes; input autocorrelation matrix; input data vector; input signal samples; quasi-Newton method; sub-blocks; third order adaptive Volterra filters; Adaptive algorithm; Adaptive filters; Algorithm design and analysis; Autocorrelation; Computational complexity; Convergence; Eigenvalues and eigenfunctions; Least squares approximation; Nonlinear filters; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on

Conference_Location

Seattle, WA

ISSN

1520-6149

Print_ISBN

0-7803-4428-6

Type

conf

DOI

10.1109/ICASSP.1998.681710

Filename

681710