Reference:
Zs. Lendek,
R. Babuska, and
B. De Schutter,
"State estimation under uncertainty: A survey," Tech. rep. 06-004,
Delft Center for Systems and Control, Delft University of Technology,
Delft, The Netherlands, 65 pp., Feb. 2006.
Abstract:
This report gives an overview of the state-of-the-art in state
estimation under uncertainty. More specifically, we discuss two
probabilistic state estimation methods: Kalman filters and particle
filters, and several types of fuzzy and neural observers.