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Navigation Integration Using the Fuzzy Strong Tracking Unscented Kalman Filter

Published online by Cambridge University Press:  12 March 2009

Dah-Jing Jwo  and
Shih-Yao Lai
Dah-Jing Jwo*
Affiliation:
(Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University)
Shih-Yao Lai
Affiliation:
(Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University)
*
(Email: djjwo@mail.ntou.edu.tw)
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Abstract

A navigation integration processing scheme, called the strong tracking unscented Kalman filter (STUKF), is based on the combination of an unscented Kalman filter (UKF) and a strong tracking filter (STF). The UKF employs a set of sigma points by deterministic sampling, such that the linearization process is not necessary, and therefore the error caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. As a type of adaptive filter, the STF is essentially a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. In order to resolve the shortcoming in traditional approach for selecting the softening factor through personal experience or computer simulation, a novel scheme called the fuzzy strong tracking unscented Kalman filter (FSTUKF) is presented where the Fuzzy Logic Adaptive System (FLAS) is incorporated for determining the softening factor. The proposed FSTUKF algorithm shows promising results in estimation accuracy when applied to the integrated navigation system design, as compared to the EKF, UKF and STUKF approaches.

Keywords

Integrated navigationUnscented Kalman filterStrong tracking filterFuzzy logic
Type
Research Article
Information
The Journal of Navigation , Volume 62 , Issue 2 , April 2009 , pp. 303 - 322
Copyright
Copyright © The Royal Institute of Navigation 2009

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