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research-article

Enhanced Polynomial Chaos-Based Extended Kalman Filter Technique for Parameter Estimation

[+] Author and Article Information
Jeremy Kolansky

Student Member of ASME Advanced Vehicle Dynamics Laboratory Mechanical Engineering Dept., Virginia Tech 9L Randolph Hall, 460 Old Turner Street, Blacksburg, Virginia 24061
JKolansk@vt.edu

Corina Sandu

ASME Fellow Director, Advanced Vehicle Dynamics Laboratory Professor, Mechanical Eng. Dept., Virginia Tech 104 Randolph Hall, 460 Old Turner Street Blacksburg, Virginia 24061
csandu@vt.edu

1Corresponding author.

ASME doi:10.1115/1.4031194 History: Received June 05, 2014; Revised July 24, 2015

Abstract

The Generalized Polynomial Chaos mathematical technique, when integrated with the Extended Kalman Filter method, provides a parameter estimation and state tracking method. The truncation of the series expansions degrades the link between parameter convergence and parameter uncertainty which the filter uses to perform the estimations. An empirically derived correction for this problem is implemented, that maintains the original parameter distributions. A comparison is performed to illustrate the improvements of the proposed approach. The method is demonstrated for parameter estimation on a regression system, where it is compared to the Recursive Least Squares method.

Copyright (c) 2015 by ASME
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