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)


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.


Particle Image Velocimetry Observation Model Forecast Ensemble Proposal Distribution Kalman Gain 
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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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