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Abstract
Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
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.
Resources
Lecture Notes
Lecture notes available on course website:http://www.idsc.ethz.ch/education/lectures/recursive-estimation.html