• DocumentCode
    441660
  • Title

    Particle Filter Based on Strong Tracking Filter

  • Author

    Deng, Xiao-Long ; Guo, Wei-Zhong ; Xie, Jian-Yin ; Liu, Jun

  • Volume
    1
  • fYear
    2005
  • fDate
    18-21 Aug. 2005
  • Firstpage
    658
  • Lastpage
    661
  • Abstract
    One of the key issues for particle filter is the proposal distribution. A new proposal distribution, the strong tracking filter (STF) proposal distribution, is presented. The time-varied fading factor in the STF that can be tuned on line makes the algorithm adaptive. In the bearings-only passive target tracking examples, the simulation results confirm the efficiency of particle filter with the new proposal distribution.
  • Keywords
    particle filter; proposal distribution; strong tracking filter; target tracking; Adaptive filters; Density functional theory; Density measurement; Filtering theory; Gaussian processes; Mechanical engineering; Particle filters; Particle tracking; Proposals; Target tracking; particle filter; proposal distribution; strong tracking filter; target tracking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
  • Conference_Location
    Guangzhou, China
  • Print_ISBN
    0-7803-9091-1
  • Type

    conf

  • DOI
    10.1109/ICMLC.2005.1527026
  • Filename
    1527026
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=441660