Cart (Loading....) | Create Account
Close category search window
 

Blind source separation using clustering-based multivariate density estimation algorithm

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

6 Author(s)
Zhenya He ; Dept. of Radio Eng., Southeast Univ., Nanjing, China ; Luxi Yang ; Ju Liu ; Ziyi Lu
more authors

A learning algorithm is developed for blind separation of the independent source signals from their linear mixtures. The algorithm is based on minimizing a contrast function defined in terms of the Kullback-Leibler distance. We use a clustering-based multivariate density estimation approach to reduce the number of the parameters to be updated. Simulations illustrate the validity of the algorithm

Published in:

Signal Processing, IEEE Transactions on  (Volume:48 ,  Issue: 2 )

Date of Publication:

Feb 2000

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.