FEL3320 Applied Estimation 7.5 credits

Tillämpad estimering

The overall goal of the course is to give the participants theoretical as well practical skills and experience in estimation applications. In estimation we try to determine some property of a system or some other quantity based on noise corrupted measurements from sensors. The course will start from a number concrete examples taken from e.g. robitics and target tracking, to motivate the need for various filtering techniques such as Kalman filters and particle filters.

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Course information

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.

Course Disposition

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Literature and preparations

Specific prerequisites *

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Recommended prerequisites

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

Equipment

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Literature

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Examination and completion

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

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Opportunity to raise an approved grade via renewed examination

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Examiner

John Folkesson

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 FEL3320

Offered by

EECS/Decision and Control Systems

Main field of study *

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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)

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.

Postgraduate course

Postgraduate courses at EECS/Decision and Control Systems