• DocumentCode
    3573227
  • Title

    Interacting multiple model algorithm based on S-amended UKF

  • Author

    Zhang Yuan ; Dong Shou-quan ; Yang Xing-bao ; Liu Shu-bo ; Chu Jun-bo

  • Author_Institution
    Coll. of Inf. Sci. & Technol., Dalian Maritime Univ., Dalian, China
  • fYear
    2014
  • Firstpage
    3805
  • Lastpage
    3809
  • Abstract
    Devoted to the problem of maneuvering target tracking under nonlinear observation, an S-amended unscented Kalman filtering (SUKF) is developed in this paper, which uses the idea of S-amended anti-divergent method for Kalman filter in linear filtering and improves the performance of unscented Kalman filtering algorithm. Then based on the coordinated turn (CT) models, adopting the method of SUKF, an interacting multiple model (IMM) algorithm named interacting multiple model algorithm based on S-amended unscented Kalman filtering (IMM-SUKF) is researched. Simulation results show that this algorithm can effectively improve the tracking precision of the multiple model algorithm, especially when the model mismatching and the target´s sudden maneuvering occurs, and that it is suitable for engineering applications, especially for tracking highly maneuvering aerial targets.
  • Keywords
    Kalman filters; nonlinear filters; target tracking; S-amended UKF; S-amended unscented Kalman filtering; coordinated turn models; interacting multiple model algorithm; linear filtering; multiple model algorithm; nonlinear observation; target tracking; Atmospheric modeling; Educational institutions; Filtering algorithms; Kalman filters; Maximum likelihood detection; Target tracking; S-amended; interacting multiple model (IMM); maneuvering target tracking; unscented Kalman filter (UKF);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2014 11th World Congress on
  • Type

    conf

  • DOI
    10.1109/WCICA.2014.7053351
  • Filename
    7053351