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

Separation of an instantaneous mixture of Gaussian autoregressive sources by the exact maximum likelihood approach

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

2 Author(s)
Degerine, S. ; LMC-IMAG, Grenoble, France ; Zaidi, A.

This paper deals with the problem of blind separation of an instantaneous mixture of Gaussian autoregressive sources, without additive noise, by the exact maximum likelihood approach. The maximization of the likelihood function is divided, using relaxation, into two suboptimization problems, solved by relaxation methods as well. The first one consists of the estimation of the separating matrix when the autoregressive structure of the sources is fixed. The second one aims at estimating this structure when the separating matrix is fixed. We show that the first problem is equivalent to the determinant maximization of the separating matrix under nonlinear constraints. We prove the existence and the consistency of the maximum likelihood estimator. We also give the expression of Fisher's information matrix. Then, we study, by computer simulations, the performance of our estimator and show the improvement of its achievements w.r.t. both quasimaximum likelihood and second-order blind identification (SOBI) estimators.

Published in:

Signal Processing, IEEE Transactions on  (Volume:52 ,  Issue: 6 )

Date of Publication:

June 2004

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.