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Quasi-Gaussian Particle Filtering

  • Yuanxin Wu
  • Dewen Hu
  • Meiping Wu
  • Xiaoping Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3991)

Abstract

The recently-raised Gaussian particle filtering (GPF) introduced the idea of Bayesian sampling into Gaussian filters. This note proposes to generalize the GPF by further relaxing the Gaussian restriction on the prior probability. Allowing the non-Gaussianity of the prior probability, the generalized GPF is provably superior to the original one. Numerical results show that better performance is obtained with considerably reduced computational burden.

Keywords

Mean Square Error Posterior Probability Prior Probability Unscented Kalman Filter Average Mean Square Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yuanxin Wu
    • 1
  • Dewen Hu
    • 1
  • Meiping Wu
    • 1
  • Xiaoping Hu
    • 1
  1. 1.Department of Automatic Control, College of Mechatronics and AutomationNational University of Defense TechnologyChangshaP.R. China

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