By Topic

Maximum likelihood estimation of a class of non-Gaussian densities with application to Ip deconvolution

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)
Pham, T.T. ; Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA ; Defigueiredo, R.J.P.

The properties of the maximum likelihood estimator of the generalized p-Gaussian (GPG) probability density function from N independent identically distributed samples is investigated, especially in the context of the deconvolution problem under GPG white noise. Specifically, the properties in the estimator are first described independently of the application. Then the solution of the above-mentioned deconvolution problem is obtained as the solution of a minimum norm problem in an lp normed space. It is shown that such minimum norm solution is the maximum-likelihood estimate of the system function parameters and that such an estimate is unbiased, with the lower bound of the variance of the error equal to the Cramer-Rao lower bound, and the upper bound derived from the concept of a generalized inverse. The results are illustrated by computer simulations

Published in:

Acoustics, Speech and Signal Processing, IEEE Transactions on  (Volume:37 ,  Issue: 1 )