Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
Lernziel
Learn the basic recursive estimation methods and their underlying principles.
Inhalt
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.
Die Leistungskontrolle wird nur in der Session nach der Lerneinheit angeboten. Die Repetition ist nur nach erneuter Belegung möglich.
Prüfungsmodus
schriftlich 150 Minuten
Zusatzinformation zum Prüfungsmodus
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.
Hilfsmittel schriftlich
One A4 sheet of paper (2 pages, handwritten or computer typed)
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.