NDLI: Noise Covariance Identification Based Adaptive UKF with Application to Mobile Robot Systems

Content Provider	IEEE Xplore Digital Library
Author	Qi Song Zhe Jiang Jianda Han
Copyright Year	2007
Description	Author affiliation: Shenyang Inst. of Autom., Chinese Acad. of Sci., Shenyang (Qi Song; Zhe Jiang; Jianda Han)
Abstract	A novel adaptive unscented Kalman filter (UKF) based on dual estimation structure is proposed. The filter is composed of two parallel master-slave UKFs, while the master one estimates the states and the slave one estimates the diagonal elements of the noise covariance matrix for the master UKF. By estimating the noise covariance online, the proposed method is able to compensate the errors resulting from the change of the noise statistics. Such a mechanism improves the adaptive ability of the UKF and enlarges its application scope. Simulations conducted on the dynamics of an omni-directional mobile robot indicate that the performance of the adaptive UKF is superior to the standard one in terms of fast convergence and estimation accuracy.
Sponsorship	IEEE Robotics and Automation Soc
Starting Page	4164
Ending Page	4169
File Size	602913
Page Count	6
File Format	PDF
ISBN	1424406013
ISSN	10504729
DOI	10.1109/ROBOT.2007.364119
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-10
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mobile robots Master-slave State estimation Robotics and automation Covariance matrix Vehicle dynamics Error analysis Convergence Automatic control Neural networks
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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