Performance evaluation of nonlinearity and non-Gaussianity measures in state estimation

J. Dunik, O. Straka, A. F. Garcia-Fernandez

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

6 Citations (Scopus)

Abstract

The paper deals with the state estimation of nonlinear stochastic dynamic systems. The stress is laid on the assessment of the estimate error, which is caused by the violation of the estimator design assumptions. The assessment is based on measures comparing estimators actual working conditions and the assumptions under which the estimators have been proposed. In particular, the measures of nonlinearity and non-Gaussianity are discussed. The measures are briefly introduced and selected typical representatives are detailed with respect to their implementation. Performance of the measures is evaluated in the framework of the Gaussian filters in a numerical study.

Original languageEnglish
Title of host publication20th International Conference on Information Fusion, Fusion 2017 - Proceedings
PublisherIEEE
Pages564-573
Number of pages10
ISBN (Electronic)9780996452700
DOIs
Publication statusPublished - 11 Aug 2017
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Information Fusion - Xian, China, Xian, China
Duration: 10 Jul 201713 Jul 2017
Conference number: 20
http://www.fusion2017.org/

Conference

ConferenceInternational Conference on Information Fusion
Abbreviated titleFUSION
CountryChina
CityXian
Period10/07/201713/07/2017
Internet address

Keywords

  • Measures of nonlinearity and non-Gaussianity
  • Non-Gaussian filtering
  • Nonlinear filtering

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