State estimation in high dimensional systems: the method of the ensemble unscented Kalman filter
Seminar Room 1, Newton Institute
The ensemble Kalman filter (EnKF) is a Monte Carlo implementation of the Kalman filter, which is often adopted to reduce the computational cost when dealing with high dimensional systems. In this work, we propose a new EnKF scheme based on the concept of the unscented transform, which therefore will be called the ensemble unscented Kalman filter (EnUKF). Under the assumption of Gaussian distribution of the estimation errors, it can be shown analytically that, the EnUKF can achieve more accurate estimations of the ensemble mean and covariance than the ordinary EnKF. Therefore incorporating the unscented transform into an EnKF may benefit its performance. Numerical experiments conducted on a $40$-dimensional system support this argument.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.