• DocumentCode
    2670340
  • Title

    Filter design methods of multiple model system

  • Author

    Dong, Yan ; Hongyue, Zhang

  • Author_Institution
    Dept. of Autom. Control, Beijing Univ. of Aeronaut. & Astronaut., China
  • fYear
    1994
  • fDate
    2-5 Oct 1994
  • Firstpage
    59
  • Lastpage
    63
  • Abstract
    In this paper, two filter design methods of multiple model system are proposed. One is the identification of ARMA model, and the other is χ2 test. The identification of ARMA model means the steady state gain matrix of Kalman filter can be identified online via recursive extended least squares method, by comparison of steady-state Kalman filter gain with the Kalman filter gain obtained from possible model, the true gain matrix can be determined by the principle of minimal error norm. The χ2 test method means the true model can be determined by detection of the whiteness of innovations process. The two methods are applied to homing guidance system. The simulation results prove that both methods are effective
  • Keywords
    Kalman filters; autoregressive moving average processes; filtering theory; least squares approximations; matrix algebra; missile guidance; state estimation; χ2 test; ARMA model; Kalman filter; filter design; homing guidance system; identification; innovations process whiteness; minimal error norm; multiple model system; recursive extended least squares method; state estimation; steady state gain matrix; Adaptive filters; Control systems; Design methodology; Equations; Kalman filters; Polynomials; State estimation; Steady-state; Technological innovation; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multisensor Fusion and Integration for Intelligent Systems, 1994. IEEE International Conference on MFI '94.
  • Conference_Location
    Las Vegas, NV
  • Print_ISBN
    0-7803-2072-7
  • Type

    conf

  • DOI
    10.1109/MFI.1994.398480
  • Filename
    398480