• DocumentCode
    3580392
  • Title

    An algorithm based on interacting multiple models for maneuvering target tracking

  • Author

    Xulong Chen ; Jian Gao ; Xing Han

  • Author_Institution
    Xi´an Electron. Eng. Res. Inst., Xi´an, China
  • fYear
    2014
  • Firstpage
    405
  • Lastpage
    408
  • Abstract
    Kanlman filtering algorithm is commonly used as radar target tracking algorithm. In allusion to the problems caused by the filter divergence and inapposite model parameters of Kaiman filtering, such as low target tracking precision, this paper proposes an adaptive tracking algorithm with Markov probability, namely Interacting Multiple Models (IMM) algorithm, to improve the radar target tracking precision. IMM algorithm can efficiently track one maneuvering target and then realize the adaptive tracking of the target. Simulation results show that IMM algorithm has perfect tracking stability and high tracking precision.
  • Keywords
    Kalman filters; Markov processes; target tracking; IMM algorithm; Kalman filtering algorithm; Markov probability; adaptive target tracking; adaptive tracking algorithm; filter divergence; interacting multiple models algorithm; low target tracking precision; maneuvering target tracking; radar target tracking algorithm; Algorithm design and analysis; Kalman filters; Radar tracking; Standards; Target tracking; Interacting Multiple Model; Kaiman filtering; adaptive tracking; target tracking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Technology and Artificial Intelligence Conference (ITAIC), 2014 IEEE 7th Joint International
  • Print_ISBN
    978-1-4799-4420-0
  • Type

    conf

  • DOI
    10.1109/ITAIC.2014.7065080
  • Filename
    7065080