@Article{NMTMA-6-21,
author = {},
title = {Fluid Flow Estimation with Multiscale Ensemble Filters Based on Motion Measurements Under Location Uncertainty},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2013},
volume = {6},
number = {1},
pages = {21--46},
abstract = {<p style="text-align: justify;">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 multiscale 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.</p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.2013.mssvm02},
url = {http://global-sci.org/intro/article_detail/nmtma/5893.html}
}