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

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Recursive Estimation

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

VVZ CR 3.0

Last Updated: 2026-02-05 15:42:01

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.Additionally, there will be two continuous performance assessment tasks during the semester, both optional and only contributing to the final grade if they help improve it.The quiz is an optional, interim examination roughly in the middle of the semester. It tests the student's understanding of the topics covered so far. It contributes 20% to the final grade, but only if it helps improve the final grade.The programming assignment is an optional learning task in the last third of the semester. It requires the student to understand and apply the lecture material. It contributes a maximum of 0.25 grade points to the final grade.

Course Components

Type	Title	Time & Place	Hours
lecture	Recursive Estimation The lecture starts in the second week of the Semester.	Wed 13:00-15:00 (ER SA TZ) Wed 13:15-15:00 (HG F 1)	2 h weekly
exercise	Recursive Estimation The exercise starts in the second week of the Semester.	Wed 15:00-16:00 (ER SA TZ) Wed 15:15-16:00 (HG F 1)	1 h weekly

Offered In

Computational Science and Engineering Bachelor
- For All Programme Regulations
  - Fields of Specialization
    - Robotics
Mechanical Engineering Master
- Core Courses
  - Mechanics, Materials, Structures (The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor.)
  - Robotics, Systems and Control (The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor. .)
Computational Science and Engineering Master
- Fields of Specialization
  - Robotics
Mathematics Master
- Application Area (Only necessary and eligible for the Master degree in Applied Mathematics. One of the application areas specified must be selected for the category Application Area for the Master degree in Applied Mathematics. At least 8 credits are required in the chosen application area.)
  - Control and Automation
Computational Biology and Bioinformatics Master (More informations at: )
- Advanced Courses (A total of 30 ECTS needs to be acquired in the Advanced Courses category. Thereof 18 ECTS in the Theory and 12 ECTS in the Biology category. Note that some of the lectures are being recorded: )
  - Theory (At least 18 ECTS need to be acquired in this category.)
Electrical Engineering and Information Technology Master
- Master Studies (Programme Regulations 2018)
  - Systems and Control (The core courses and specialization courses below are a selection for students who wish to specialize in the area of "Systems and Control", see . The individual study plan is subject to the tutor's approval.)
    - Core Courses (These core courses are particularly recommended for the field of "Systems and Control". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT.)
      - Advanced Core Courses
  - Signal Processing and Machine Learning (The core courses and specialization courses below are a selection for students who wish to specialize in the area of "Signal Processing and Machine Learning ", see . The individual study plan is subject to the tutor's approval.)
    - Specialization Courses (These specialization courses are particularly recommended for the area of "Signal Processing and Machine Learning", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialization courses during the MSc EEIT.)
- Master Studies (Programme Regulations 2008)
  - Major Courses (A total of 42 CP must be achieved form courses during the Master Program. The individual study plan is subject to the tutor's approval.)
    - Systems and Control
      - Core Subjects (These core subjects are particularly recommended for the field of "Systems and Control".)
    - Signal Processing and Machine Learning
      - Recommended Subjects
Robotics, Systems and Control Master
- Core Courses
Doctoral Department of Mechanical and Process Engineering (More Information at: )
- Doctoral and Post-Doctoral Courses
Data Science Master
- Core Courses
  - Core Electives