151-0566-00L  Recursive Estimation

SemesterSpring Semester 2018
LecturersR. D'Andrea
Periodicityyearly course
Language of instructionEnglish


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.