• DocumentCode
    1585131
  • Title

    Attitude angle aided IMMCKF algorithm

  • Author

    Hai, Chen ; Ganlin, Shan

  • Author_Institution
    Dept. of Opt. & Electron. Eng., Coll. of Mech. Eng., Shijiazhuang, China
  • Volume
    1
  • fYear
    2011
  • Firstpage
    197
  • Lastpage
    200
  • Abstract
    To effectively solve the tracking problem of nonlinear maneuvering target, interacting multiple model cubature Kalman filter (immckf) algorithm brings cubature Kalman filter (ckf) into the interacting multiple model (imm) algorithm. This paper brings the attitude angle information into the immckf algorithm, and identifies the target maneuver mode through the fuzzy association between the attitude angle and the current motion mode of target; then the association result is used to fuse with the model probability of imm to enhance its model resolving power. A simulation of maneuvering target tracking shows that the attitude angle aided immckf (aa-immckf) algorithm can effectively improve the tracking accuracy and stability of the original immckf algorithm.
  • Keywords
    Kalman filters; fuzzy set theory; probability; target tracking; IMMCKF algorithm; attitude angle information; fuzzy association; interacting multiple model cubature Kalman filter; maneuvering target tracking; model probability; nonlinear maneuvering target; target maneuver mode; target motion mode; tracking problem; Computational modeling; Kalman filters; Mathematical model; Probability; Radar tracking; Target tracking; attitude angle; cubature Kalman filter; interacting multiple model; maneuver target tracking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronic Measurement & Instruments (ICEMI), 2011 10th International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-8158-3
  • Type

    conf

  • DOI
    10.1109/ICEMI.2011.6037712
  • Filename
    6037712