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Improving Regularised Particle Filters

  • Christian Musso
  • Nadia Oudjane
  • Francois Le Gland
Chapter
Part of the Statistics for Engineering and Information Science book series (ISS)

Abstract

The optimal filter computes the posterior probability distribution of the state in a dynamical system, given noisy measurements, by iterative application of prediction steps according to the dynamics of the state, and correction steps taking the measurements into account. A new class of approximate nonlinear filter has been recently proposed, the idea being to produce a sample of independent random variables, called a particle system, (approximately) distributed according to this posterior probability distribution. The method is very easy to implement, even in high-dimensional problems, since it is sufficient in principle to simulate independent sample paths of the hidden dynamical system.

Keywords

Particle Filter Particle System Extended Kalman Filter Correction Step Rejection Correction 
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 Science+Business Media New York 2001

Authors and Affiliations

  • Christian Musso
  • Nadia Oudjane
  • Francois Le Gland

There are no affiliations available

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