The generalized polynomial chaos (gPC) mathematical technique, when integrated with the extended Kalman filter (EKF) 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, which 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 (RLS) method.
Enhanced Polynomial Chaos-Based Extended Kalman Filter Technique for Parameter Estimation
Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received June 5, 2014; final manuscript received July 24, 2015; published online November 29, 2017. Assoc. Editor: D. Dane Quinn.
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Kolansky, J., and Sandu, C. (November 29, 2017). "Enhanced Polynomial Chaos-Based Extended Kalman Filter Technique for Parameter Estimation." ASME. J. Comput. Nonlinear Dynam. February 2018; 13(2): 021012. https://doi.org/10.1115/1.4031194
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