Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
Learn the basic recursive estimation methods and their underlying principles.
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 offered every session. Repetition possible without re-enrolling for the course unit.
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 one continuous performance assessment task during the semester. This is a programming assignment. It 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. It only contributes to the final grade if it improves it.
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.