• DocumentCode
    2759852
  • Title

    PF-UKF-RJMCMC Approaches for Radar Target-Tracking

  • Author

    Huibo, Zhao ; Quan, Pan

  • Author_Institution
    Coll. of Autom., Northwestern Polytech. Univ., Xi´´an, China
  • Volume
    2
  • fYear
    2009
  • fDate
    25-26 July 2009
  • Firstpage
    373
  • Lastpage
    376
  • Abstract
    Nonlinear problem of maneuvering target is a hot and difficult topic in radar target tracking fielding. This paper outline the the pros and cons of non-linear filtering methods nowdays, emphatically analyses uncertainty sampling and random sampling method, describe Markov chain Monte Carlo algorithms, along with Reversible Jump ratio improving methods. and try to present a comprehensive picture of the performance comparison among classical tracking methods combining RJMCMC, including PF, EKF-PF, UKF-PF techniques from the varied literature that have seen wide application in radar target tracking field. Simulations on maneuvering target radar tracking are carried out to validate the performance of the proposed scheme in comparison with existing methods.
  • Keywords
    Kalman filters; Markov processes; Monte Carlo methods; nonlinear filters; particle filtering (numerical methods); radar tracking; random processes; target tracking; Markov chain Monte Carlo algorithm; nonlinear filtering method; particle filter; radar target-tracking; random sampling; reversible jump MCMC; reversible jump ratio; target maneuvering; uncertainty sampling; unscented Kalman filter; Automation; Educational institutions; Filtering; Kalman filters; Particle filters; Radar tracking; Sampling methods; Space technology; Target tracking; Uncertainty; RJMCMC; UPF; nonlinear; target tracking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Technology and Computer Science, 2009. ITCS 2009. International Conference on
  • Conference_Location
    Kiev
  • Print_ISBN
    978-0-7695-3688-0
  • Type

    conf

  • DOI
    10.1109/ITCS.2009.214
  • Filename
    5190257