Skip to main content
Till KTH:s startsida

EL2320 Applied Estimation 7.5 credits

Administer About course

This course mainly focuses on estimation using Kalman filter and particle filter. 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.

Information per course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Termin

Information for Autumn 2025 Start 27 Oct 2025 programme students

Course location

KTH Campus

Duration
27 Oct 2025 - 12 Jan 2026
Periods
P2 (7.5 hp)
Pace of study

50%

Application code

50375

Form of study

Normal Daytime

Language of instruction

English

Number of places

Places are not limited

Target group

Open to all programmes, as long as it can be included in your programme.

Planned modular schedule
[object Object]
Schedule
Schedule is not published

Contact

Examiner
No information inserted
Course coordinator
No information inserted
Teachers
No information inserted

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus EL2320 (Autumn 2024–)
Headings with content from the Course syllabus EL2320 (Autumn 2024–) 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.

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.

Literature

You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.

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

  • KON1 - Written partial exams, 3.5 credits, grading scale: P, F
  • PRO1 - Project, 2.0 credits, grading scale: P, F
  • PRO2 - Project, 2.0 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 possibility of re-examination of all written partial exams (KON1) is given during the examination period at the end of the course.

The final mark is based on how the well student has carried out KON1, PRO1 and PRO2 in combination.

Examiner

Profile picture John Folkesson

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 room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

EECS/Intelligent Systems

Main field of study

Electrical Engineering

Education cycle

Second cycle

Transitional regulations

The previous module TEN1 has been replaced by KON1.

Supplementary information

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