Orthogonalization techniques for adaptive filters

The rate of convergence and the computational complexity of an adaptive algorithm are two essential criteria by which the performance of an adaptive filter is measured. These objectives conflict with one another; each property is successfully achieved at the expense of the other. The principal means of achieving rapid convergence is to decouple and normalize the eigenvalues governing the solution evolution. Given a suitable structure, it is possible to derive an orthogonalizing algorithm with O(N) computations. However, such algorithms currently suffer from numerical instability or require computationally expensive operations, such as square root and division.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Hull, Andrew William

Contributors dc:contributor

• Jenkins, W. Kenneth

• Engineering, Electronics and Electrical
• Engineering, System Science

Statement dc:rights

• Copyright 1994 Hull, Andrew William

Language dc:language

eng

Identifier

AAI9416375
(UMI)AAI9416375

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/19077