• DocumentCode
    1503800
  • Title

    Box Gaussian Mixture Filter

  • Author

    Ali-Löytty, Simo Sakari

  • Author_Institution
    Dept. of Math., Tampere Univ. of Technol., Tampere, Finland
  • Volume
    55
  • Issue
    9
  • fYear
    2010
  • Firstpage
    2165
  • Lastpage
    2169
  • Abstract
    This note presents the box Gaussian mixture filter (BGMF), which is an efficient filter for the systems with mainly linear measurements but enables utilizing highly nonlinear measurements. BGMF contains a new way to approximate the prior distributions with a Gaussian mixture, whose components have small covariances. In this note we present results on the weak convergence of BGMF. In simulations, we see that in our hybrid position example BGMF outperforms the conventional particle filter.
  • Keywords
    Gaussian processes; Kalman filters; filtering theory; Gaussian distribution; box Gaussian mixture filter; extended Kalman filter; filter banks; filtering techniques; nonlinear measurements; Convergence; Filter bank; Filtering theory; Gaussian distribution; Gaussian noise; Noise measurement; Nonlinear filters; Particle filters; Statistics; Time measurement; Extended Kalman filter; Gaussian distribution; filter banks; filtering techniques; filtering theory;
  • fLanguage
    English
  • Journal_Title
    Automatic Control, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9286
  • Type

    jour

  • DOI
    10.1109/TAC.2010.2051486
  • Filename
    5473096