151-0566-00L  Recursive Estimation

SemesterSpring Semester 2017
LecturersR. D'Andrea
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
151-0566-00 VRecursive Estimation
The lecture starts in the second week of the semester.
2 hrs
Wed13:15-15:00CHN C 14 »
R. D'Andrea
151-0566-00 URecursive Estimation
The exercise starts in the second week of the semester.
1 hrs
Wed15:15-16:00CHN C 14 »
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: Link
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 examinationThe final grade is based on the session exam, an optional in-class quiz, and optional programming exercises: The grade of the quiz may contribute 20% to the final grade, but only if it helps improving the final grade. The average grade of the programming exercises may contribute 20% to the final grade, but only if it helps improving 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.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Doctoral Department of Mechanical and Process EngineeringDoctoral and Post-Doctoral CoursesWInformation
Electrical Engineering and Information Technology MasterCore SubjectsWInformation
Mechanical Engineering MasterMechanics, Materials, StructuresWInformation
Mechanical Engineering MasterRobotics, Systems and ControlWInformation
Mathematics MasterControl and AutomationWInformation
Computational Science and Engineering BachelorRoboticsWInformation
Computational Science and Engineering MasterRoboticsWInformation
Robotics, Systems and Control MasterCore CoursesWInformation