• DocumentCode
    3136108
  • Title

    Data-driven method for Kalman filtering

  • Author

    Xie, Wen ; Xia, Yuanqing

  • Author_Institution
    Sch. of Autom., Beijing Inst. of Technol., Beijing, China
  • Volume
    2
  • fYear
    2011
  • fDate
    25-28 July 2011
  • Firstpage
    830
  • Lastpage
    835
  • Abstract
    In this paper, the state estimation problem is considered based on the input-output data. A data-driven subspace identification method combined with the Kalman on-line filtering algorithm is proposed for solving the state estimation problem for a class of dynamical systems where the exact models can not be established. Simulation results are further presented to show the effectiveness of the proposed strategy.
  • Keywords
    Kalman filters; nonlinear dynamical systems; state estimation; Kalman on-line filtering; data-driven subspace identification; dynamical systems; input-output data; state estimation; Computational modeling; Equations; Kalman filters; Mathematical model; Observability; Prediction algorithms; State estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Information Processing (ICICIP), 2011 2nd International Conference on
  • Conference_Location
    Harbin
  • Print_ISBN
    978-1-4577-0813-8
  • Type

    conf

  • DOI
    10.1109/ICICIP.2011.6008364
  • Filename
    6008364
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3136108