Adaptive cubature strong tracking information filter using variational Bayesian method

Quanbo Ge, Chenglin Wen, Shaodong Chen, Ruoyu Sun, Yuan Li

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

Abstract

For most practical nonlinear state estimation problems, the conventional nonlinear filters do not usually work well for some cases, such as inaccurate system model, sudden change of state-interested and unknown variance of measurement noise. In this paper, an adaptive cubature strong tracking information filter using variational Bayesian (VB) method is proposed to cope with these complex cases. Firstly, the strong tracking filtering (STF) technology is used to integrally improve performance of cubature information filter (CIF) aiming at the first two cases and an iterative scheme is presented to effectively evaluate a strong tracking fading factor. Secondly, the VB method is introduced to iteratively evaluate the unknown variance of measurement noise. Finally, the novel adaptive cubature information filter is obtained by perfectly combining the STF technology with the VB method, where the variance estimation provided by the VB method guarantees normal running of the strong tracking functionality.

Original languageEnglish (US)
Title of host publication19th IFAC World Congress IFAC 2014, Proceedings
EditorsEdward Boje, Xiaohua Xia
PublisherIFAC Secretariat
Pages5945-5950
Number of pages6
ISBN (Electronic)9783902823625
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
Event19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014 - Cape Town, South Africa
Duration: Aug 24 2014Aug 29 2014

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume19
ISSN (Print)1474-6670

Other

Other19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014
CountrySouth Africa
CityCape Town
Period8/24/148/29/14

Keywords

  • Cubature information filter
  • Nonlinear system
  • Strong tracking filtering
  • Unknown variance of measurement noise
  • Variational Bayesian

ASJC Scopus subject areas

  • Control and Systems Engineering

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  • Cite this

    Ge, Q., Wen, C., Chen, S., Sun, R., & Li, Y. (2014). Adaptive cubature strong tracking information filter using variational Bayesian method. In E. Boje, & X. Xia (Eds.), 19th IFAC World Congress IFAC 2014, Proceedings (pp. 5945-5950). (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 19). IFAC Secretariat. https://doi.org/10.3182/20140824-6-za-1003.00558