Sigma point and particle approximations of stochastic differential equations in optimal filtering
published: Aug. 5, 2008, recorded: May 2008, views: 1068
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
The unscented transform (UT) is a relatively recent method for approximating non-linear transformations of random variables. Instead of the classical Taylor series approximations, it is based on forming a set of sigma points, which are propagated through the non-linearity. The unscented Kalman filter (UKF) is an alternative to the extended Kalman filter (EKF), which utilizes the unscented transform in the filter computations. However, in its original form, the UKF is a discrete-time algorithm and it cannot be directly applied to estimation problems, where the state dynamics are modeled in continuous-time as stochastic differential equations. In the talk I will review the Taylor series, sigma-point (unscented) and particle approximations of stochastic differential equations in optimal (Bayesian) filtering context and present some applications of the methods in navigation systems and in monitoring of chemical processes.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !