• DocumentCode
    2526421
  • Title

    An Innovation Filtering Multiple Model Algorithm for Integrated Navigation System

  • Author

    Rong-chun, Zang ; Ping-yuan, Cui

  • Author_Institution
    Harbin Inst. of Technol.
  • Volume
    3
  • fYear
    2006
  • fDate
    Aug. 30 2006-Sept. 1 2006
  • Firstpage
    394
  • Lastpage
    397
  • Abstract
    An interacting multiple model unscented Kalman filter (IMM-UKF) was proposed for the two problems of the nonlinear filtering i.e. nonlinearity and noise. The uncertainty of the noise can be described by a set of switching models. This modeling approach makes it possible to employ the multiple model estimation (MME) combining with UKF to deal with the problem of nonlinear filtering with uncertainty noise. An innovation filtering is introduced in the MME, which can decrease the measurement noise and derive more accurate statistic information for weight calculation. The application of the algorithm on GPS/DR integrated navigation system demonstrated that the method was feasible and accurate
  • Keywords
    Global Positioning System; Kalman filters; estimation theory; noise; nonlinear filters; GPS-DR integrated navigation system; innovation filtering multiple model algorithm; interacting multiple model unscented Kalman filter; measurement noise; multiple model estimation; nonlinear filtering; switching models; uncertainty noise; Filtering algorithms; Gaussian distribution; Information filtering; Information filters; Jacobian matrices; Navigation; Noise measurement; Nonlinear filters; Sampling methods; Technological innovation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    0-7695-2616-0
  • Type

    conf

  • DOI
    10.1109/ICICIC.2006.417
  • Filename
    1692197