  We investigate the combined use of a Kinect depth sensor and of a stochastic
data assimilation 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 data assimilation
scheme is enhanced with the use of two observations instead of one classically.
We evaluate the developed approach on two numerical test cases: a collapse of a
water column as a toy-example and a flow in an suddenly expanding flume as a
more realistic flow. The robustness of the method to depth data errors and also
to initial and inflow conditions is considered. We illustrate the interest of
using two observations instead of one observation into the correction step,
especially for unknown inflow boundary conditions. Then, the performance of the
Kinect sensor to capture temporal sequences of depth observations is
investigated. Finally, the efficiency of the algorithm is qualified for a wave
in a real rectangular flat bottom tank. It is shown that for basic initial
conditions, the particle filter rapidly and remarkably reconstructs velocity
and height of the free surface flow based on noisy measurements of the
elevation alone.
