Weighted ensemble transform Kalman filter for image assimilation

Béyou, Sébastien; Cuzol, Anne; Gorthi, Sai Subrahmanyam; Mémin, Étienne

doi:10.3402/tellusa.v65i0.18803

Cited by 25 publications

(23 citation statements)

References 38 publications

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“…Other methods, such as the deterministic EnKF (Sakov and Oke, 2008), correct the members obtained after the analysis step so that the covariance matrices computed from these members become closer to the ones modeled with the Kalman filter. Beyou et al (Beyou et al, 2012) apply EnKF for motion estimation. Each motion member is written as the sum of a mean and a perturbation.…”

Section: Introductionmentioning

confidence: 99%

“…The deterministic EnKF computes the covariance matrix associated to the perturbations after the analysis step, conducting to an overload of computations. Due to the resampling process applied in (Beyou et al, 2012), the error covariance matrices, computed from the ensemble members, do not characterize the uncertainty on motion estimation. Consequently, these three approaches are not used in the following.…”

Section: Introductionmentioning

confidence: 99%

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An Image-based Ensemble Kalman Filter for Motion Estimation

Lepoittevin

Herlin

Béréziat³

2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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Abstract:This paper designs an Image-based Ensemble Kalman Filter (IEnKF), whose components are defined only from image properties, to estimate motion on image sequences. The key elements of this filter are, first, the construction of the initial ensemble, and second, the propagation in time of this ensemble on the studied temporal interval. Both are analyzed in the paper and their impact on results is discussed with synthetic and real data experiments. The initial ensemble is obtained by adding a Gaussian vector field to an estimate of motion on the first two frames. The standard deviation of this normal law is computed from motion results given by a set of optical flow methods of the literature. It describes the uncertainty on the motion value at initial date. The propagation in time of the ensemble members relies on the following evolution laws: transport by velocity of the image brightness function and Euler equations for the motion function. Shrinking of the ensemble is avoided thanks to a localization method and the use of observation ensembles, both techniques being defined from image characteristics. This Image-based Ensemble Kalman Filter is quantified on synthetic experiments and applied on traffic and meteorological images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Image-based Ensemble Kalman Filter for Motion Estimation

Lepoittevin

Herlin

Béréziat³

2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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show abstract

“…This approach is close to the technique proposed in Van Leeuwen (2010). A variation of a similar technique based on a deterministic square-root formulation is also described in Beyou et al (2013). In order to make the estimation of the filtering distribution exact (up to the sampling), each member of the ensemble must be weighted at each observation instant t k with appropriate weights w (i) t k , defined from Eq.…”

Section: Weighted Ensemble Kalman Filter (Wenkf)mentioning

confidence: 99%

Monte Carlo fixed-lag smoothing in state-space models

Cuzol

Mémin

2014

Nonlin. Processes Geophys.

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Abstract. This paper presents an algorithm for Monte Carlo fixed-lag smoothing in state-space models defined by a diffusion process observed through noisy discrete-time measurements. Based on a particle approximation of the filtering and smoothing distributions, the method relies on a simulation technique of conditioned diffusions. The proposed sequential smoother can be applied to general nonlinear and multidimensional models, like the ones used in environmental applications. The smoothing of a turbulent flow in a highdimensional context is given as a practical example.

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“…The data assimilation (DA) approach can indeed serve this purpose. Note that image data assimilation is a research topic drawing more and more attention since the last decades and several strategies have been proposed to deal with these difficulties (Papadakis and Mémin, 2008;Corpetti et al, 2009;Cuzol and Mémin, 2009;Titaud et al, 2010;Beyou et al, 2013;Huot et al, 2013;Bereziat and Herlin, 2015;Chabot et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

High-resolution data assimilation through stochastic subgrid tensor and parameter estimation from 4DEnVar

Yin¹,

Mémin

2017

Tellus A: Dynamic Meteorology and Oceanography

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In this paper, we explore a dynamical formulation allowing to assimilate high-resolution data in a large-scale fluid flow model. This large-scale formulation relies on a random modelling of the small-scale velocity component and allows to take into account the scale discrepancy between the dynamics and the observations. It introduces a subgrid stress tensor that naturally emerges from a modified Reynolds transport theorem adapted to this stochastic representation of the flow. This principle is used within a stochastic shallow water model coupled with an 4DEnVar assimilation technique to estimate both the flow initial conditions and the inhomogeneous time-varying subgrid parameters. The performance of this modelling has been assessed numerically with both synthetic and real-world data. Our strategy has shown to be very effective in providing a more relevant prior/posterior ensemble in terms of the dispersion compared to other tests using the standard shallow water equations with no subgrid parameterization or with simple eddy viscosity models. We also compared two localization techniques. The results indicate the localized covariance approach is more suitable to deal with the scale discrepancy-related errors.

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Weighted ensemble transform Kalman filter for image assimilation

Cited by 25 publications

References 38 publications

An Image-based Ensemble Kalman Filter for Motion Estimation

An Image-based Ensemble Kalman Filter for Motion Estimation

Monte Carlo fixed-lag smoothing in state-space models

High-resolution data assimilation through stochastic subgrid tensor and parameter estimation from 4DEnVar

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