Covariance correction filter with unknown disturbance associated to system state

Yonggang Wang, Xiaoxu Wang, Quan Pan, Yan Liang

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

5 Scopus citations

Abstract

This paper is motivated by the fact that the unknown disturbances (UDs) in discrete-time stochastic systems may be associated with state, such as the perturbation and the model error. In such case, the UD takes on the first two moment (FTM) at least, i.e. the property of both mean and covariance. If use the UD-FTM to correct the state estimation and its covariance simultaneously, it should result in a better accuracy than the classical methods including the augmentation, robust filter and interacting multiple model (IMM), which only consider the first moment (mean) property of UD to correct the state estimation, regardless of the second moment (covariance) of UD. In this paper, a two-stage expectation maximization (EM) algorithm is proposed to jointly identify the UD-FTM. The first EM is for joint state estimation and UD's pseudo measurement (UD-PM) identification, while the second EM is for Gaussian mixture (GM), which uses the identified UD-PM from the first EM to fit out the UD-FTM. Further we can improve the state estimation accuracy by using the fitted UD-FTM with an open-loop correction. Finally, simulation results illustrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3632-3637
Number of pages6
ISBN (Electronic)9781467386821
DOIs
StatePublished - 28 Jul 2016
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: 6 Jul 20168 Jul 2016

Publication series

NameProceedings of the American Control Conference
Volume2016-July
ISSN (Print)0743-1619

Conference

Conference2016 American Control Conference, ACC 2016
Country/TerritoryUnited States
CityBoston
Period6/07/168/07/16

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