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.
KTH Campus
50%
50237
Normal Daytime
English
Places are not limited
Open to all programmes, as long as it can be included in your programme.
Master's Programme, Computer Science, åk 2, CSCS, Recommended
Master's Programme, Computer Science, åk 2, CSDA, Recommended
Master's Programme, ICT Innovation, åk 2, AUSM, Recommended
Master's Programme, ICT Innovation, åk 2, AUSY, Recommended
Master's Programme, Industrial Engineering and Management, åk 1, MAIG, Conditionally Elective
Master's Programme, Machine Learning, åk 1, Conditionally Elective
Master's Programme, Machine Learning, åk 2, Conditionally Elective
Master's Programme, Systems, Control and Robotics, åk 1, RASM, Mandatory
John Folkesson (johnf@kth.se)
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus EL2320 (Autumn 2024–)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.
After passing the course, the student shall be able to
in order to be able to work with estimation.
Knowledge in probability theory and statistics, 6 higher education credits, equivalent to completed course SF1910-SF1924/SF1935.
Courses corresponding to SF1624 Algebra and Geometry, SF1901 Probability Theory and Statistics, SF1635 Signals and Systems, part I. Being able to program in MATLAB.
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.
If the course is discontinued, students may request to be examined during the following two academic years.
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.
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
The previous module TEN1 has been replaced by KON1.
In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex.
