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
There is a written final exam during the examination session, which covers all material taught during the course, i.e. the material presented during the lectures and corresponding problem sets, programming exercises, and recitations. Additionally, there will be two continuous performance assessment tasks during the semester, both optional and only contributing to the final grade if they help improve it. The quiz is an optional, interim examination roughly in the middle of the semester. It tests the student's understanding of the topics covered so far. It contributes 20% to the final grade, but only if it helps improve the final grade. The programming assignment is an optional learning task in the last third of the semester. It requires the student to understand and apply the lecture material. It contributes a maximum of 0.25 grade points to 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.
Performance assessment as a semester course