• DocumentCode
    2293917
  • Title

    What is the ensemble Kalman filter and how well does it work?

  • Author

    Gillijns, S. ; Mendoza, O. Barrero ; Chandrasekar, J. ; De Moor, B.L.R. ; Bernstein, D.S. ; Ridley, A.

  • Author_Institution
    Katholieke Universiteit, Leuven
  • fYear
    2006
  • fDate
    14-16 June 2006
  • Abstract
    In this paper we described the ensemble Kalman filter algorithm. This approach to nonlinear Kalman filtering is a Monte Carlo procedure, which has been widely used in weather forecasting applications. Our goal was to apply the ensemble Kalman filter to representative examples to quantify the tradeoff between estimation accuracy and ensemble size. For all of the linear and nonlinear examples that we considered, the ensemble Kalman filter worked successfully once a threshold ensemble size was reached. In future work we will investigate the factors that determine this threshold value
  • Keywords
    Kalman filters; Monte Carlo methods; estimation theory; nonlinear filters; weather forecasting; Monte Carlo; ensemble Kalman filter algorithm; ensemble size; estimation accuracy; nonlinear Kalman filtering; weather forecasting; Covariance matrix; Gaussian noise; Jacobian matrices; Linear systems; Nonlinear dynamical systems; Nonlinear systems; Particle filters; Riccati equations; State estimation; Weather forecasting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference, 2006
  • Conference_Location
    Minneapolis, MN
  • Print_ISBN
    1-4244-0209-3
  • Electronic_ISBN
    1-4244-0209-3
  • Type

    conf

  • DOI
    10.1109/ACC.2006.1657419
  • Filename
    1657419