151-0566-00L  Recursive Estimation

SemesterSpring Semester 2020
LecturersR. D'Andrea
Periodicityyearly recurring course
Language of instructionEnglish


151-0566-00 VRecursive Estimation
The lecture starts in the second week of the Semester.
2 hrs
Wed13-15ER SA TZ »
13-15HG F 1 »
R. D'Andrea
151-0566-00 URecursive Estimation
The exercise starts in the second week of the Semester.
1 hrs
Wed15-16ER SA TZ »
15-16HG F 1 »
R. D'Andrea

Catalogue data

AbstractEstimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
ObjectiveLearn the basic recursive estimation methods and their underlying principles.
ContentIntroduction 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.
Lecture notesLecture notes available on course website: http://www.idsc.ethz.ch/education/lectures/recursive-estimation.html
Prerequisites / NoticeRequirements: Introductory probability theory and matrix-vector algebra.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersR. D'Andrea
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 150 minutes
Additional information on mode of examinationThere 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 aidsOne 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.

Learning materials

Main linkWebsite
Only public learning materials are listed.


No information on groups available.


There are no additional restrictions for the registration.

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