By Topic

Statistical estimation with 1/f-type prior models: robustness to mismatch and efficient model determination

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)
Dufour, R.M., Jr. ; Dept. of Electr. & Comput. Eng., Northeastern Univ., Boston, MA, USA ; Miller, E.L.

One common problem in signal and image processing is the determination of a discrete representation of a signal given data corresponding to a noisy and blurred version of the unknown. We examine the performance degradation associated with the use of a two-parameter family of fractal-type statistical models in a linear least squares estimator (LLSE) when the model parameters do not match those of the actual process. These models are shown to perform well under various circumstances for estimating noisy and blurred 1/f type fractals and first order Gauss-Markov (FOGM) signals. In addition we demonstrate an effective means of bounding one of the model parameters as a function of the other so as to reduce the model to a single parameter

Published in:

Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on  (Volume:5 )

Date of Conference:

7-10 May 1996