• DocumentCode
    1417682
  • Title

    The Multiple Model CPHD Tracker

  • Author

    Georgescu, Ramona ; Willett, Peter

  • Author_Institution
    Electr. & Comput. Eng. Dept., Univ. of Connecticut, Storrs, CT, USA
  • Volume
    60
  • Issue
    4
  • fYear
    2012
  • fDate
    4/1/2012 12:00:00 AM
  • Firstpage
    1741
  • Lastpage
    1751
  • Abstract
    The probability hypothesis density (PHD) is a practical approximation to the full Bayesian multi-target filter. The cardinalized PHD (CPHD) filter was proposed to deal with the “target death” problem of the PHD filter. A multiple-model PHD exists; in this work, a multiple model version of the considerably more complex CPHD filter is derived. It is implemented using Gaussian mixtures, and a track management (for display and scoring) strategy is developed.
  • Keywords
    Bayes methods; Gaussian processes; target tracking; Bayesian multitarget filter; Gaussian mixtures; multiple model CPHD tracker; probability hypothesis density; target death problem; track management; Bayesian methods; Equations; Hidden Markov models; Joints; Mathematical model; Target tracking; CPHD; Cardinalized probability hypothesis density filter; MMCPHD; MMPHD; PHD; multiple model;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/TSP.2012.2183128
  • Filename
    6126062
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1417682