Princeton University

School of Engineering & Applied Science

Nonlinear State Estimation based on Deterministic Sampling

Uwe D. Hanebeck, Karlsruhe Institute of Technology, Germany
E-Quad, B205
Tuesday, December 10, 2013 - 4:30pm to 5:30pm

Abstract:  This talk is about designing nonlinear filters for estimating the state of nonlinear stochastic systems based on approximating prior densities by deterministic samples called Dirac mixture approximation. As an example, a new Gaussian filter is derived that relies on two main ingredients: i) the progressive inclusion of the measurement information and ii) a tight coupling between a Gaussian density and its deterministic Dirac mixture approximation. No second Gaussian assumption for the joint density of state and measurement is required, so that the performance is much better than that of Linear Regression Kalman Filters (LRKFs), which heavily rely on this assumption. It can be used as a plug-in replacement for standard Gaussian filters such as the Unscented Kalman Filter (UKF).
Bio:  Uwe D. Hanebeck is a chaired professor of Computer Science at the Karlsruhe Institute of Technology (KIT) in Germany and director of the Intelligent Sensor-Actuator-Systems Laboratory (ISAS). Since 2005, he is the chairman of the Research Training Group RTG 1194 “Self-Organizing Sensor-Actuator-Networks”  financed by the German Research Foundation.  Prof. Hanebeck obtained his Ph.D. degree in 1997 and his habilitation degree in 2003, both in Electrical Engineering from the Technical University in Munich, Germany. His research interests are in the areas of information fusion, nonlinear state estimation, stochastic modeling, system identification, and control.