• DocumentCode
    115747
  • Title

    An extended Kalman filter with a computed mean square error bound

  • Author

    Hexner, G. ; Weiss, H.

  • Author_Institution
    Adv. Defense Syst., RAFAEL, Haifa, Israel
  • fYear
    2014
  • fDate
    15-17 Dec. 2014
  • Firstpage
    5008
  • Lastpage
    5014
  • Abstract
    The paper proposes a new recursive filter for non-linear systems that inherently computes a valid bound on the mean square estimation error. The proposed filter, bound based extended Kalman, (BEKF) is in the form of an extended Kalman filter. The main difference of the proposed filter from the conventional extended Kalman filter is in the use of a computed mean square error bound matrix, to calculate the filter gain, and to serve as bound on the actual mean square error. The paper shows that when the system is linear the proposed filtering algorithm reduces to the conventional Kalman filter. The theory presented in the paper is applicable to a wide class of systems, but if the system is polynomial, then the recently developed theory of positive polynomials considerably simplifies the filter´s implementation.
  • Keywords
    Kalman filters; matrix algebra; mean square error methods; nonlinear control systems; polynomials; recursive filters; BEKF; extended Kalman filter; filtering algorithm; mean square error bound matrix; nonlinear system; polynomials; recursive filter; Covariance matrices; Estimation error; Kalman filters; Mean square error methods; Polynomials; Standards; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-1-4799-7746-8
  • Type

    conf

  • DOI
    10.1109/CDC.2014.7040171
  • Filename
    7040171