Perfect Reconstruction Filter Banks for Adaptive Filtering and Coding

This thesis considers the design of perfect reconstruction filter banks (PRFBs) for adaptive filtering and coding applications. The first half of this thesis derives a generalization to the Transform Domain Adaptive Filter (TDAF), which uses filter banks instead of unitary transforms to preprocess input signals. The parametrization for a class of nonparaunitary PRFBs is derived and used to express the minimum mean square error (MMSE) of the Generalized Transform Domain Adaptive Filters, in terms of the filter bank chosen. Using this parametrization, it is shown how to analytically design filter banks to give optimum error and convergence performances given prior knowledge of the adaptive application. Design examples demonstrate the improved error performance of the derived structures relative to the Least Mean Square (LMS) algorithm when prior knowledge is incorporated.

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

2014

Author dc:creator

• Usevitch, Bryan Edward

Contributors dc:contributor

• Cybenko, G.,

• Engineering, Electronics and Electrical

Identifier

(UMI)AAI9329188

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/72008