Multiscale Weighted Ensemble Kalman Filter for Fluid Flow Estimation

  • Sai Gorthi
  • Sébastien Beyou
  • Thomas Corpetti
  • Etienne Mémin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6667)

Abstract

This paper proposes a novel multi-scale fluid flow data assimilation approach, which integrates and complements the advantages of a Bayesian sequential assimilation technique, the Weighted Ensemble Kalman filter (WEnKF) [12], and an improved multiscale stochastic formulation of the Lucas-Kanade (LK) estimator. The proposed scheme enables to enforce a physically plausible dynamical consistency of the estimated motion fields along the image sequence.

Keywords

Particle Image Velocimetry Observation Model Forecast Ensemble Proposal Distribution Kalman Gain 
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 2012

Authors and Affiliations

  • Sai Gorthi
    • 1
    • 2
  • Sébastien Beyou
    • 1
    • 2
  • Thomas Corpetti
    • 1
    • 2
  • Etienne Mémin
    • 1
    • 2
  1. 1.INRIA / FLUMINANCERennes CedexFrance
  2. 2.CNRS/LIAMABeijingPR China

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