• DocumentCode
    180565
  • Title

    On the L4 convergence of particle filters with general importance distributions

  • Author

    Mbalawata, Isambi S. ; Sarkka, Simo

  • Author_Institution
    Lappeenranta Univ. of Technol., Lappeenranta, Finland
  • fYear
    2014
  • fDate
    4-9 May 2014
  • Firstpage
    8048
  • Lastpage
    8052
  • Abstract
    In this paper we extend the L4 proof of Hu et al. (2008) from bootstrap type of particle filters to particle filters with general importance distributions. The result essentially shows that with general importance distributions the particle filter converges provided that the importance weights are bounded. By numerical simulations we also show that this condition is often also a practical requirement for a good performance of a particle filter.
  • Keywords
    bootstrapping; particle filtering (numerical methods); statistical analysis; statistical distributions; bootstrap type; general importance distributions; particle filters; Approximation methods; Atmospheric measurements; Bayes methods; Convergence; Gaussian distribution; Monte Carlo methods; Particle measurements; Particle filter; convergence; importance distribution; unbounded function;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
  • Conference_Location
    Florence
  • Type

    conf

  • DOI
    10.1109/ICASSP.2014.6855168
  • Filename
    6855168
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=180565