Performance analysis of UKF for nonlinear problems

Authors:
Guanglin Li;Fuming Sun;Na Cheng
Affiliations:
School of Electronic and Information Engineering, Liaoning University of Technology, Jinzhou, China;School of Electronic and Information Engineering, Liaoning University of Technology, Jinzhou, China;School of Electronic and Information Engineering, Liaoning University of Technology, Jinzhou, China
Venue:
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Year:
2009

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Abstract

Unscented Kalman filter (UKF) is a class of nonlinear filtering methods based on unscented transform within the Kalman filter framework. It is in light of the intuition that to approximate a probability distribution by a set of deterministic samples is easier than to approximate an arbitrary nonlinear transform. The key factors of UKF--the scalar, the state variable dimensions and the noises involved in nonlinear system, besides the probability distribution, should be synthetically analyzed. The mean square error is adopted to evaluate the effect of these factors on the performance of UKF. The simulation results show that the factors above mentioned more or less affect the performance of UKF, in which the state noise plays the most important role.