• DocumentCode
    3660294
  • Title

    A new parameters adaptively adjusting method of current statistical model

  • Author

    Yongjian Yang;Xiaoguagn Fan;Shengda Wang;Zhenfu Zhuo;Jianguo Nan;Lei Huang

  • Author_Institution
    Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi´an, Shannxi Province, 710038, China
  • fYear
    2015
  • Firstpage
    1738
  • Lastpage
    1742
  • Abstract
    The fixed maximum acceleration and maneuvering frequency of current statistical model leads to the divergence of filtering algorithm. In this study, a new model which employs innovation dominated subjection function to adaptively adjust maximum acceleration and maneuvering frequency is proposed based on current statistical model. Although the new model has a better performance, a fluctuant phenomenon appears. As far as this problem is concerned, a new filter algorithm which is based on amendatory and adaptively fading kalman filtering is proposed. The results of simulation indicate the effectiveness and coherent of the new model and the new algorithm, and their well performance in maneuvering target tracking.
  • Keywords
    "Adaptation models","Target tracking","Acceleration","Filtering algorithms","Accuracy","Filtering","Technological innovation"
  • Publisher
    ieee
  • Conference_Titel
    Information and Automation, 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/ICInfA.2015.7279568
  • Filename
    7279568