Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
Objective
Learn the basic recursive estimation methods and their underlying principles.
Content
Introduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle.
The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examination
written 150 minutes
Additional information on mode of examination
The final grade is based on the session exam, an optional in-class quiz, and optional programming exercises: The grade of the quiz may contribute 20% to the final grade, but only if it helps improving the final grade. The average grade of the programming exercises may contribute 20% to the final grade, but only if it helps improving the final grade.
Written aids
One A4 sheet of paper (2 pages, handwritten or computer typed)
This information can be updated until the beginning of the semester; information on the examination timetable is binding.