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Fluid Flow Estimation with Multiscale Ensemble Filters Based on Motion Measurements Under Location Uncertainty

Published online by Cambridge University Press:  28 May 2015

Sébastien Beyou ,
Thomas Corpetti ,
Sai Gorthi  and
Etienne Mémin
Sébastien Beyou*
Affiliation:
INRIA/FLUMINANCE, 35042 Rennes Cedex, France
Thomas Corpetti*
Affiliation:
CNRS/LETG-Rennes-Costel, Campus Villejean Place du recteur Henri Le Moal CS 24307 35043 Rennes cedex, France
Sai Gorthi*
Affiliation:
Indian Institute of Space Science and Technology, Valiamala P.O., Thiruvananthapuram - 695 547, Kerala India
Etienne Mémin*
Affiliation:
INRIA/FLUMINANCE, 35042 Rennes Cedex, France
*
Corresponding author.Email address:Sebastien.Beyou@inria.fr
Corresponding author.Email address:thomas.corpetti@univ-rennes2.fr
Corresponding author.Email address:gorthisubrahmanyam@iist.ac.in
Corresponding author.Email address:etienne.memin@inria.fr
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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) [27]. The data assimilation proposed in this work incorporates measurement brought by an efficient multiscale stochastic formulation of the well-known Lucas-Kanade (LK) estimator. This estimator has the great advantage to provide uncertainties associated to the motion measurements at different scales. The proposed assimilation scheme benefits from this multi-scale uncertainty information and enables to enforce a physically plausible dynamical consistency of the estimated motion fields along the image sequence. Experimental evaluations are presented on synthetic and real fluid flow sequences.

Keywords

65M1078A48Data assimilationstochastic filterparticle filtersfluid motion estimation
Type
Research Article
Information
Numerical Mathematics: Theory, Methods and Applications , Volume 6 , Issue 1 , February 2013 , pp. 21 - 46
Copyright
Copyright © Global Science Press Limited 2013

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