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FEL3320 Applied Estimation 7.5 credits

Information per course offering

Termin

Information for Autumn 2024 Start 28 Oct 2024 programme students

Course location

KTH Campus

Duration
28 Oct 2024 - 13 Jan 2025
Periods
P2 (7.5 hp)
Pace of study

50%

Application code

51441

Form of study

Normal Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

Places are not limited

Target group
No information inserted
Planned modular schedule
[object Object]
Part of programme
No information inserted

Contact

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

John Folkesson (johnf@kth.se)

Course syllabus as PDF

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

Course syllabus FEL3320 (Spring 2019–)
Headings with content from the Course syllabus FEL3320 (Spring 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course focuses on giving the participants practical experience in using different estimation techniques on real problems. Examples used in the course are, for example, from navigation with mobile robots.

The following will be covered in the course: Observability, the Markov assumption, data association, estimation techniques such as Kalman filter, extended Kalman filter, particle filter, Rao-Blackwellized particle filter, Unscented Kalman Filter.

Intended learning outcomes

The overall goal of the course is to give the participants theoretical as well as practical skills and experience in estimation. The course will start from a number of concrete examples to motivate the need for various filtering techniques such as Kalman filters and particle filters.  After completing the course the participants should:

  • be able to: describe the parts of a Bayesian recursive filter in terms of the underlying probabilities, compare and contrast different estimation techniques, and select and apply appropriate techniques to problems. 
  • have reflected on the relationship between measurement uncertainty, probability theory and estimation methods. 
  • have gained experience in finding information from current scientific literature including recently published journal articles. As well as presentation of results in well structured scientific reports.

Literature and preparations

Specific prerequisites

No information inserted

Recommended prerequisites

Students should have knowledge of probability theory equivalent to an undergraduate course in probability and statistics.

Equipment

No information inserted

Literature

No information inserted

Examination and completion

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

Grading scale

P, F

Examination

  • EXA1 - Examination, 7.5 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 basic part of the examination in the course consists of two lab assignments (PRO1), a project  (PRO2) and an exam (TEN1). These are credited as,

PRO1: 2.0hp 
PRO2: 2.0hp 
TEN: 3.5hp   

Passing them means that the student has passed the course. 

On the Exam the passing grade will be a score of 80% correct. To pass PRO2 a result corresponding to at least a B is required.

Other requirements for final grade

To get a passing grade in the course the students need to pass the labs, the mandatory part of the project assignments and the exam.

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 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

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third 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)

Postgraduate course

Postgraduate courses at EECS/Decision and Control Systems