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.