By Topic

A new reduced-bias multichannel gradient-based steepest descent algorithm for ill-conditioned correlation matrices

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)
Byung-Jae Kwak ; Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA ; Nah-Oak Song ; Yagle, A.E.

The convergence speed of the steepest descent (SD) adaptive algorithm is determined by the eigenvalue spread of the correlation matrix. When the correlation matrix is singular, the algorithm will take a long time to converge. The problem can be regularized by adding a small constant to the diagonal, but this comes at the price of introducing bias. This paper introduces a new adaptive algorithm that uses a family of steepest descent iterations which converge quickly while making the bias arbitrarily small. A numerical example discusses in some detail the operation of the new algorithm

Published in:

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on  (Volume:1 )

Date of Conference: