Length: 3 hours
Intended Audience: The intended audience are users and researchers of stochastic filtering dealing with uncertainties that
are not purely stochastic, such as discretization uncertainty and set-membership constraints, or are
dealing with negative information. The presented approaches will not only help them understand a
more general way to model their systems, but can also help them reduce non-linearity of their system
and measurement models. Attendants must be familiar with the Kalman filter to take full advantage
of this tutorial.
Description: perfectly known parameters of the prior and noise distributions. This requirement is not special to the
Kalman filter but is rather an inherent problem deeply rooted into Bayesian filtering and, in parts, also
frequentist statistics. The attendants will learn how this problem can be overcome by using hybrid
approaches that rely on a combination of stochastic and set-membership methods. The approach is
thoroughly explained along with solutions to new challenges arising. Furthermore, using the example
of event-based estimation, the attendants will learn how these versatile approaches not only help to
improve our modeling of the true uncertainty but also help to make use of the absence of information.
Prerequisites:
Presenter: Florian Pfaff and Benjamin Noack
Benjamin Noack received his Diploma in Computer Science at the University of Karlsruhe, Germany, in
2009. He graduated as Dr.-Ing. (Doctor of Engineering) at the Karlsruhe Institute of Technology (KIT),
Germany, in January 2013. Since 2013 he is a senior researcher at the Karlsruhe Institute of Technology
(KIT), Germany. His research interests are in the area of multi-sensor data fusion, distributed and
decentralized Kalman filtering, combined stochastic and set-membership approaches to state
estimation, and event-based systems.
Florian Pfaff started his involvement with Robust Kalman filtering during his master thesis and is coauthor
of papers presenting important advances. He started as a PHD student at the Karlsruhe Institute
of Technology (KIT) in 2014 after completing his master's degree for which he received an award due
to his outstanding performance.
