By Topic

Minimax robust deconvolution filters under stochastic parametric and noise uncertainties

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
$33 $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)
You-Li Chen ; Dept. of Electr. Eng., Nat. Tsing Hua Univ., Hsinchu, Taiwan ; Bor-Sen Chen

The author consider the design of robust deconvolution filters for linear discrete time systems with stochastic parameter and noise uncertainties. It is assumed that some large but bounded uncertainties exist in the driving and measurement noise covariances as well as the second-order statistics of stochastic parameters and initial conditions. Three kinds of minimax sensitivity criteria are used to develop the techniques to the synthesis of minimax deconvolution filters under uncertain linear stochastic systems. Their approach is based on saddle-point theory and the sensitivity analysis of Kalman filters. The design algorithms give the recursive realization of the minimax deconvolution filters for the time-varying uncertain systems under fairly general conditions. For the time-invariant uncertain case the existence and solutions of steady-state deconvolution filters are further developed. Finally, the utility of the minimax design approaches and the properties of the resulting minimax deconvolution filters are illustrated by a numerical example

Published in:

IEEE Transactions on Signal Processing  (Volume:42 ,  Issue: 1 )