Skip to main content

EL2320 Applied Estimation 7.5 credits

This course mainly focuses on estimation using Kalman filter and particle filter.  A broader course is DD2420 Probabilistic Graphical Methods, which can be a natural followup to this course but can also be taken on it's own.  So in this course you will go into depth and get very familiar with two methods that in the other course you will learn are specific examples of  wider classes of methods.

Course Structure

There are 12 lectures. Both theory and practice of estimation will be covered. Getting practical skills in anything requires you to get hands-on experience and as such the work between the lectures will be very important.

Two labs cover the Kalman and particle filters. 

For the final project, the student should work in pairs and implement an estimation method. Each student needs to write an individual report including a literature study. 

Recommended Prerequisits:

Courses corresponding to SF1624 Algebra and Geometry, SF1901 Probability Theory and Statistics,

Literature

The official course book is "Probabilistic robotics" by Thrun, Burgard and Fox, The MIT Press, ISBN 0-262-20162-3. Some of the material will be in the form of research publications. The students are assumed able to research for additional material to solve the project assignment.

Choose semester and course offering

Choose semester and course offering to see information from the correct course syllabus and course offering.

Headings with content from the Course syllabus EL2320 (Autumn 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course focuses on to give participants practical experience of to use different estimation methods on real problems. Examples that are used in the course been for example from navigation with mobile robots.

The course covers the following: Observability, the Markov assumption, data association, estimation methods such as Kalman Filter, Extended Kalman filters, particle filter, Rao-Blackwellized particle filters, Unscented Kalman filter.

Intended learning outcomes

After passing the course, the student shall be able to

  • describe the parts in recursive Bayesian filtering in terms of probabilities reflect on the relationships between measurement uncertainty, probability theory and estimation methods
  • describe parametric estimation technician and choose and apply appropriate method on problems
  • describe Monte Carlo estimation methods and choose and apply appropriate method on problems

in order to be able to work with estimation.

Course disposition

No information inserted

Literature and preparations

Specific prerequisites

Knowledge in probability theory and statistics, 6 higher education credits, equivalent to completed course SF1910-SF1924/SF1935.

Recommended prerequisites

Courses corresponding to SF1624 Algebra and Geometry, SF1901 Probability Theory and Statistics, SF1635 Signals and Systems, part I. Being able to program in MATLAB.

Equipment

No information inserted

Literature

The official course book is "Probabilistic robotics" by Thrun, Burgard and Fox, The MIT Press, ISBN 0-262-20162-3 covers most of the material in the course from a robotics points of view. Letcures notes will also be made available. This course is at advanced level so some of the material will be in the form of research publications. The students are assumed able to research for additional material to solve the project assignment.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F

Examination

  • PRO1 - Project, 2.0 credits, grading scale: P, F
  • PRO2 - Project, 2.0 credits, grading scale: P, F
  • TEN1 - Examination, 3.5 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

The final mark is based on how the well student has carried out TEN1, PRO1 and PRO2 in combination.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web EL2320

Offered by

Main field of study

Electrical Engineering

Education cycle

Second cycle

Add-on studies

There is a rich set of courses on related estimation, control, signal processing, systems theory, etc such as EQ2800 Optimal filtering, EQ2300 Digital signal processing, EQ2400 Adaptive signal processing, SF2842 Mathematical Systems Theory, EQ2810 Estimation theory

Contact

John Folkesson (johnf@kth.se)

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex.