NDLI: Fuzzy variant of a statistical test point Kalman filter

Content Provider	IEEE Xplore Digital Library
Author	Hudas, G. Overholt, J. Cheok, K.C.
Copyright Year	2005
Description	Author affiliation: US Army RDECOM-TARDEC, Warren, MI, USA (Hudas, G.; Overholt, J.)
Abstract	In this paper, we propose the conceptual use of fuzzy clustering techniques as iterative spatial methods to estimate a posteriori statistics in place of the weighted averaging scheme of the unscented Kalman filter. Specifically, instead of a linearization methodology involving the statistical linear regression of the process and measurement functions through some deterministically chosen set of test points (sigma points) contained within the "uncertainty region" around the state estimate, we present a variant of the unscented transformation involving fuzzy clustering techniques which will be applied to the test points yielding "degrees of membership" in which Gaussian shapes can be "fit" using a least squares scheme. Implementation into the Kalman methodology will be shown along with low-dimension state and parameter estimation examples.
Starting Page	127
Ending Page	132
File Size	1931331
Page Count	6
File Format	PDF
ISBN	078039187X
DOI	10.1109/NAFIPS.2005.1548520
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Testing Shape measurement Iterative methods Statistics Linear regression State estimation Yield estimation Fuzzy sets Least squares approximation Kalman filters
Content Type	Text
Resource Type	Article

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