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151-0566-00L 4 Credits BSC , DR , MSC D-BSSE , D-MAVT , D-INFK , D-MATH , D-PHYS , D-ITET , D-BAUG , D-HEST

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Recursive Estimation

Lecturers & Examiners: Prof. Dr. Raffaello D'Andrea

VVZ CR 3.0

Last Updated: 2026-02-05 16:38:50

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

Learning Materials (Links)

Main link
Website

General Information

Language: English
Levels: BSC , DR , MSC
Frequency: Yearly recurring

Examination

Type: session examination
Mode: written 150 minutes
Aids: One A4 sheet of paper (2 pages, handwritten or computer typed)

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.

Course Components

Type	Title	Time & Place	Hours
lecture	Recursive Estimation	Wed 14:15-16:00 (HG F 1)	2 h weekly
exercise	Recursive Estimation	Wed 16:15-17:00 (HG F 1)	1 h weekly

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