A state estimation approach based on stochastic expansions

R. H. Lopez, J. E.Souza Cursi, A. G. Carlon*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

This paper presents a new approach for state estimation problems. It is based on the representation of random variables using stochastic functions. Its main idea is to expand the state variables in terms of the noise variables of the system, and then estimate the unnoisy value of the state variables by taking the mean value of the stochastic expansion. Moreover, it was shown that in some situations, the proposed approach may be adapted to the determination of the probability distribution of the state noise. For the determination of the coefficients of the expansions, we present three approaches: moment matching (MM), collocation (COL) and variational (VAR). In the numerical analysis section, three examples are analyzed including a discrete linear system, the Influenza in a boarding school and the state estimation problem in the Hodgkin–Huxley’s model. In all these examples, the proposed approach was able to estimate the values of the state variables with precision, i.e., with very low RMS values.

Original languageEnglish (US)
Pages (from-to)3399-3430
Number of pages32
JournalComputational and Applied Mathematics
Volume37
Issue number3
DOIs
StatePublished - Jul 1 2018

Bibliographical note

Publisher Copyright:
© 2017, SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional.

Keywords

  • Polynomial chaos
  • State estimation
  • Uncertainty quantification

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics

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