• DocumentCode
    637562
  • Title

    Sufficiently informative measurements for stability of approximate conditional mean estimators

  • Author

    Techakesari, Onvaree ; Ford, Jason J.

  • Author_Institution
    Sci. & Eng. Fac., Queensland Univ. of Technologly, Brisbane, QLD, Australia
  • fYear
    2012
  • fDate
    15-16 Nov. 2012
  • Firstpage
    241
  • Lastpage
    246
  • Abstract
    This paper establishes sufficient conditions to bound the error in perturbed conditional mean estimates derived from a perturbed model (only the scalar case is shown in this paper but a similar result is expected to hold for the vector case). The results established here extend recent stability results on approximating information state filter recursions to stability results on the approximate conditional mean estimates. The presented filter stability results provide bounds for a wide variety of model error situations.
  • Keywords
    filtering theory; approximate conditional mean estimators; filter stability results; information state filter recursions; informative measurements; model error situations; perturbed conditional mean estimates; perturbed model; Approximation methods; Asymptotic stability; Computational modeling; Hidden Markov models; Noise; Stability analysis; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference (AUCC), 2012 2nd Australian
  • Conference_Location
    Sydney, NSW
  • Print_ISBN
    978-1-922107-63-3
  • Type

    conf

  • Filename
    6613203