Multiscale Weighted Ensemble Kalman Filter for Fluid Flow Estimation

Abstract

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.

Data assimilation with state alignment using high-level image structures detection

“…As a consequence, sequential and variational assimilation methods have been used for the direct estimation of the underlying dynamics given a sequence of images. Numerous works including Ensemble Kalman Filter [8] or 4D-var [20] have then been proposed to directly link the dynamics of the image sequence with the velocity of the physical model. Such approaches use the optical flow relations based on the conservation of the luminance along the image sequence.…”