This paper proposes a novel multi-scale fluid flow data assimilation approach,which integrates and complements the advantages of a Bayesian sequential assimilationtechnique, the Weighted Ensemble Kalman filter (WEnKF)...This paper proposes a novel multi-scale fluid flow data assimilation approach,which integrates and complements the advantages of a Bayesian sequential assimilationtechnique, the Weighted Ensemble Kalman filter (WEnKF) [27]. The data assimilation proposed in this work incorporates measurement brought by an efficient multiscalestochastic formulation of the well-known Lucas-Kanade (LK) estimator. This estimatorhas 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 dynamicalconsistency of the estimated motion fields along the image sequence. Experimentalevaluations are presented on synthetic and real fluid flow sequences.展开更多
基金The authors acknowledge the support of the French Agence Nationale de la Recherche(ANR),under grant PREVASSEMBLE(ANR-08-COSI-012).
文摘This paper proposes a novel multi-scale fluid flow data assimilation approach,which integrates and complements the advantages of a Bayesian sequential assimilationtechnique, the Weighted Ensemble Kalman filter (WEnKF) [27]. The data assimilation proposed in this work incorporates measurement brought by an efficient multiscalestochastic formulation of the well-known Lucas-Kanade (LK) estimator. This estimatorhas 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 dynamicalconsistency of the estimated motion fields along the image sequence. Experimentalevaluations are presented on synthetic and real fluid flow sequences.
