A particle filter to reconstruct a free-surface flow from a depth camera

  title={A particle filter to reconstruct a free-surface flow from a depth camera},
  author={Beno{\^i}t Comb{\`e}s and Dominique Heitz and Anthony Guibert and Etienne M'emin},
  journal={Fluid Dynamics Research},
We investigate the combined use of a kinect depth sensor and of a stochastic data assimilation (DA) method to recover free-surface flows. More specifically, we use a weighted ensemble Kalman filter method to reconstruct the complete state of free-surface flows from a sequence of depth images only. This particle filter accounts for model and observations errors. This DA scheme is enhanced with the use of two observations instead of one classically. We evaluate the developed approach on two… 

Study of variational ensemble methods for image assimilation

The hybrid methods combing the 4D variational method and the ensemble Kalman filter provide a flexible framework. In such framework the potential advantages with respect to each method (e.g. the

Visual inference of flow flux via free surface PDE model and image sequence assimilation

The free-surface flows, such as flows in rivers, lakes, and tides, play an important role in hydraulic engineering and environmental monitoring. However, due to their complex and changeable

Turbulent complex flows reconstruction via data assimilation in large eddy models

This thesis attempts at redressing various limitations of the assimilation procedure in order to facilitate its wider use in fluid mechanics and provides the means to estimate the background covariance matrix which is essential for an efficient assimilation algorithm.

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

Abstract 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

Small-scale reconstruction in three-dimensional Kolmogorov flows using four-dimensional variational data assimilation

Significant insights in computational fluid dynamics have been obtained in recent years by adopting the data assimilation methods developed in the meteorology community. We apply the four-dimensional

Robust flow reconstruction from limited measurements via sparse representation

It is found that sparse representation has considerably improved estimation accuracy and robustness to noise and corruption compared with least squares methods, and is a promising framework for extracting useful information from complex flow fields with realistic measurements.

Free-surface flow measurements by non-intrusive methods: a survey

Measurement of the free surface shape and its evolution in time and/or in space is of great interest in many engineering/research applications. The optical measurements of a liquid interface over an



A Method for Assimilating Lagrangian Data into a Shallow-Water-Equation Ocean Model

Lagrangian measurements provide a significant portion of the data collected in the ocean. Difficulties arise in their assimilation, however, since Lagrangian data are described in a moving frame of

Reduced-order Kalman-filtered hybrid simulation combining particle tracking velocimetry and direct numerical simulation

Abstract The capability of state-of-the-art techniques integrating experimental and computational fluid dynamics has been expanding recently. In our previous study, we have developed a hybrid

A Stochastic Filtering Technique for Fluid Flow Velocity Fields Tracking

  • A. CuzolÉ. Mémin
  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2009
The technique is formalized within a sequential Bayesian filtering framework, relying on dynamical systems theory, and the efficiency of the tracking method is demonstrated on synthetic and real-world sequences.

Data assimilation with the weighted ensemble Kalman filter

Abstract In this paper, two data assimilation methods based on sequential Monte Carlo sampling are studied and compared: the ensemble Kalman filter and the particle filter. Each of these techniques

Free-surface flows from Kinect: Feasibility and limits

In this work, we investigate the combined use of a Kinect depth sensor and of a stochastic data assimilation method to recover free-surface flows. For this purpose, we first show that the Kinect is

A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation

An ensemble Kalman filter may be considered for the 4D assimilation of atmospheric data. In this paper, an efficient implementation of the analysis step of the filter is proposed. It employs a Schur

Free surface measurement by stereo-refraction

An optical method for the measurement of the surface topography, height and normal, and the velocity in free surface flows is presented. This method of surface reconstruction is based on the analysis

Particle Kalman Filtering for Data Assimilation in Meteorology and Oceanography

We describe a discrete solution of the optimal nonlinear filter that generalizes the optimality of the correction step of the ensemble Kalman filters to nonlinear systems. This approach is based on a

Data Assimilation Schemes For Non-linear Shallow Water Flow Models

Two new algorithms are proposed, that extend the idea of the Reduced Rank Square Root filter for use with non-linear models, based on a low rank approximation of the error covariance matrix and use a square root representation of theerror covariance.

A data-assimilation method for Reynolds-averaged Navier–Stokes-driven mean flow reconstruction

Abstract We present a data-assimilation technique based on a variational formulation and a Lagrange multipliers approach to enforce the Navier–Stokes equations. A general operator (referred to as the