Polynomial chaos based method for state and parameter estimation

Reza Madankan, Puneet Singla, Tarunraj Singh, Peter Scott

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper presents a method for state and parameter estimation based on generalized polynomial chaos theory and Bayes' theorem. Generalized polynomial chaos theory (gPC) is used to propagate the joint probability density functions (pdfs) for parameter and state through forward dynamic model while the Bayes' rule is used to fuse the prior pdfs obtained through the gPC process with sensor observations to characterize non-Gaussian posterior density functions for state and parameters. Furthermore, a minimum variance based estimator is also derived which makes use of the gPC process to compute the mean and variance of actual non-Gaussian pdf. Numerical experiments involving two benchmark problems are considered to illustrate the effectiveness of the proposed ideas.

Original languageEnglish (US)
Title of host publication2012 American Control Conference, ACC 2012
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6358-6363
Number of pages6
ISBN (Print)9781457710957
DOIs
StatePublished - 2012
Event2012 American Control Conference, ACC 2012 - Montreal, QC, Canada
Duration: Jun 27 2012Jun 29 2012

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2012 American Control Conference, ACC 2012
CountryCanada
CityMontreal, QC
Period6/27/126/29/12

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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