By Topic

An improved approximate QR-LS based second-order Volterra filter

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Yi Zhou ; Dept. of Electr. & Electron. Eng., Hong Kong Univ., China ; Chan, S.C. ; Ho, K.L.

This paper proposes a new transform-domain approximate QR least-squares-based (TA-QR-LS) algorithm for adaptive Volterra filtering (AVF). It improves the approximate QR least-squares (A-QR-LS) algorithm for multichannel adaptive filtering by introducing a unitary transformation to decorrelate the input signal vector so as to achieve better convergence and tracking performances. Further, the Givens rotation is used instead of the Householder transformation to reduce the arithmetic complexity. Simulation results show that the proposed algorithm has much better initial convergence and steady state performance than the LMS-based algorithm. The fast RLS AVF algorithm [J. Lee and V. J. Mathews, Mar 1993] was found to exhibit superior steady state performance when the forgetting factor is chosen to be 0.995, but the tracking performance of the TA-QR-LS algorithm was found to be considerably better.

Published in:

Statistical Signal Processing, 2003 IEEE Workshop on

Date of Conference:

28 Sept.-1 Oct. 2003