• DocumentCode
    1658255
  • Title

    Parameter estimation in a general state space model from short observation data: A SMC based approach

  • Author

    Saha, S. ; Mandal, P.K. ; Bagchi, A. ; Boers, Y. ; Driessen, H.

  • Author_Institution
    Dept. Of Appl. Math., Univ. of Twente, Netherlands
  • fYear
    2009
  • Firstpage
    41
  • Lastpage
    44
  • Abstract
    In this article, we propose a SMC based method for estimating the static parameter of a general state space model. The proposed method is based on maximizing the joint likelihood of the observation and unknown state sequence with respect to both the unknown parameters and the unknown state sequence. This in turn, casts the problem into simultaneous estimations of state and parameter. We show the efficacy of this method by numerical simulation results.
  • Keywords
    Monte Carlo methods; maximum likelihood estimation; sequential Monte Carlo method; state space model; static parameter estimation; Mathematical model; Mathematics; Maximum likelihood estimation; Monte Carlo methods; Numerical simulation; Parameter estimation; Particle filters; Sliding mode control; State estimation; State-space methods; parameter estimation; particle filter; sequential Monte Carlo;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Statistical Signal Processing, 2009. SSP '09. IEEE/SP 15th Workshop on
  • Conference_Location
    Cardiff
  • Print_ISBN
    978-1-4244-2709-3
  • Electronic_ISBN
    978-1-4244-2711-6
  • Type

    conf

  • DOI
    10.1109/SSP.2009.5278643
  • Filename
    5278643
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1658255