• DocumentCode
    834278
  • Title

    Interacting multiple model particle filter

  • Author

    Boers, Y. ; Driessen, J.N.

  • Volume
    150
  • Issue
    5
  • fYear
    2003
  • Firstpage
    344
  • Lastpage
    349
  • Abstract
    A new method for multiple model particle (nonlinear) filtering for Markovian switching systems is presented. This new method is a combination of the interacting multiple model (IMM) filter and a (regularised) particle filter. The mixing and interaction is similar to that in a conventional IMM filter. However, in every mode a regularised particle filter is running. The regularised particle filter probability density is a mixture of Gaussian probability densities. The proposed method is able to deal with nonlinearities and non-Gaussian noise. Furthermore, the new method keeps a fixed number of particles in each mode, and therefore it does not suffer from the potential drawbacks of existing multiple model particle filters for Markovian switching systems.
  • Keywords
    Gaussian distribution; Markov processes; filtering theory; nonlinear filters; random noise; target tracking; Gaussian probability densities; IMM filter; Markovian switching systems; interacting multiple model filter; nonGaussian noise; nonlinear filter; nonlinearities; particle filter; target tracking;
  • fLanguage
    English
  • Journal_Title
    Radar, Sonar and Navigation, IEE Proceedings -
  • Publisher
    iet
  • ISSN
    1350-2395
  • Type

    jour

  • DOI
    10.1049/ip-rsn:20030741
  • Filename
    1249153