151-0566-00L  Recursive Estimation

Courses

Catalogue data

Abstract	Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
Learning 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.
Lecture notes	Lecture notes available on course website: http://www.idsc.ethz.ch/education/lectures/recursive-estimation.html
Prerequisites / Notice	Requirements: Introductory probability theory and matrix-vector algebra.
Competencies	Subject-specific Competencies Concepts and Theories assessed Techniques and Technologies assessed Method-specific Competencies Analytical Competencies assessed Decision-making assessed Media and Digital Technologies fostered Problem-solving assessed Project Management fostered Social Competencies Communication fostered Cooperation and Teamwork fostered Customer Orientation fostered Leadership and Responsibility fostered Self-presentation and Social Influence fostered Sensitivity to Diversity fostered Negotiation fostered Personal Competencies Integrity and Work Ethics fostered

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits	4 credits
Examiners	R. D'Andrea
Type	session examination
Language of examination	English
Repetition	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.
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.

Learning materials

Groups

Restrictions

Offered in

Programme	Section	Type
Biomedical Engineering Master	Recommended Elective Courses	W
Computational Biology and Bioinformatics Master	Theory	W
Data Science Master	Subject-Specific Electives	W
Data Science Master	Interdisciplinary Electives	W
Data Science Master	Core Electives	W
Doctorate Mechanical and Process Engineering	Subject Specialisation	W
Electrical Engineering and Information Technology Master	Core Subjects	W
Electrical Engineering and Information Technology Master	Advanced Core Courses	W
Electrical Engineering and Information Technology Master	Specialization Courses	W
Electrical Engineering and Information Technology Master	Recommended Subjects	W
Geomatics Master	Major in Space Geodesy and Navigation	W
Geomatics Master	Complementary Electives	W
Mechanical Engineering Master	Mechanics, Materials, Structures	W
Mechanical Engineering Master	Robotics, Systems and Control	W
Mathematics Master	Control and Automation	W
Computational Science and Engineering Bachelor	Robotics	W
Computational Science and Engineering Master	Robotics	W
Robotics, Systems and Control Master	Core Courses	W