FEL3320 Applied Estimation 7.5 credits
Information for research students about course offerings
The course runs in parallel with the advanced level course EL2320 with the same name and is offered P2 every year.
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Application
For course offering
Autumn 2023 Start 30 Oct 2023 programme students
Application code
51566
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
Recommended prerequisites
Students should have knowledge of probability theory equivalent to an undergraduate course in probability and statistics.
Equipment
Literature
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
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
Opportunity to raise an approved grade via renewed examination
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
Offered by
Main field of study
Education 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