By Topic

High Order Filters for Estimation in Non-Gaussian Noise

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)
Thomopoulos, S.C.A. ; Decision and Control Systems Laboratory, Dept. of Electrical and Computer Engineering, The Pennsylvania State University, University Park, PA 16802 ; Hilands, Thomas W.

In this paper high order vector filter equations are developed for estimation in non-Gaussian noise. The difference between the filters developed here and the standard Kalman filter is that the filter equation contains nonlinear functions of the innovations process. These filters are general in that the initial state covariance, the measurement noise covariance, and the process noise covariance can all have non-Gaussian distributions. Two filter structures are developed. The first filter is designed for systems with asymmetric probability densities. The second is designed for systems with symmetric probability densities. Experimental evaluation of these filters for estimation in non-Gaussian noise, formed from Gaussian sum distributions, shows that these filters perform much better than the standard Kalman filter, and close to the optimal Bayesian estimator.

Published in:

American Control Conference, 1993

Date of Conference:

2-4 June 1993