• DocumentCode
    558648
  • Title

    Nonlinear estimation using Mean Field Games

  • Author

    Pequito, Sergio ; Aguiar, A. Pedro ; Sinopoli, Bruno ; Gomes, Diogo A.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Tech. Univ. of Lisbon, Lisbon, Portugal
  • fYear
    2011
  • fDate
    12-14 Oct. 2011
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    This paper introduces Mean Field Games (MFG) as a framework to develop optimal estimators in some sense for a general class of nonlinear systems. We show that under suitable conditions the estimation error converges exponentially fast to zero. Computer simulations are performed to illustrate the method. In particular we provide an example where the proposed estimator converges whereas both extended Kalman filter and particle filter diverge.
  • Keywords
    Kalman filters; game theory; nonlinear estimation; particle filtering (numerical methods); MFG; extended Kalman filter; mean field games; nonlinear estimation; optimal estimator; particle filter; Convergence; Cost function; Equations; Estimation; Games; Kalman filters; Mathematical model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Network Games, Control and Optimization (NetGCooP), 2011 5th International Conference on
  • Conference_Location
    Paris
  • Print_ISBN
    978-1-4673-0383-5
  • Type

    conf

  • Filename
    6103897
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=558648