Prerequisite/Recommended prerequisite for
participation in the module
Numerical methods, probability, statistics and stochastic
processes.
Content, progress and pedagogy of the
module
Purpose
- to contribute to students’ attainment of knowledge and
comprehension of Kalman filter theory.
- to contribute to students’ attainment of knowledge and
comprehension of how to apply Kalman filter theory for engineering
problems, such as abnormal diagnosis and multiple target tracking
etc.
Content
Conventional Kalman filter theory:
- Scale Kalman filter
- Vector-based Kalman filter
- Convergence and preconditions
Extended Kalman filter theory:
- Extended Kalman filter (EKF)
- Uncended Kalman filter (UKF)
- Multi-mode Kalman filter
Application of KF theory
- Fault detection using KF theory
- Fault diagnosis using KF theory
- Multiple target tracking
- Multi-mode system estimation
Learning objectives
Knowledge
- Have knowledge and comprehension for Kalman filter theory
- Have knowledge and comprehension for extended Kalman filter
techniques
- Have knowledge and comprehension for vector-based Kalman filter
theory.
- Have comprehension of the application of Kalman filter theory
to abnormal scenario diagnosis
- Have comprehension of the application of Kalman filter theory
to multiple target tracking
Skills
- Be able to apply Kalman filter theory for state estimation
problem in the presence of noises.
- Be able to apply Kalman filter theory for abnormal diagnosis
problem
- Be able to apply Kalman filter theory for multiple target
tracking problem
- Be able to judge the usefulness of the set up methods
- Be able to relate the methods to applications in the
industry
Competences
- Independently be able to define and analyze scientific problems
using Kalman filter theory
- Independently be able to apply Kalman filter theory for
different engineering problems
Type of instruction
The program is based on a combination of academic,
problem-oriented and interdisciplinary approaches and organized
based on the following work and evaluation methods that combine
skills and reflection:
- Lectures
- Classroom instruction
- Project work
- Workshops
- Exercises (individually and in groups)
- Teacher feedback
- Reflection
- Portfolio work
Extent and expected workload
Since it is a 5 ECTS course module, the work load is expected to
be 150 hours for the student
Exam
Exams
Name of exam | Kalman Filter Theory and its Application |
Type of exam | Written or oral exam |
ECTS | 5 |
Assessment | 7-point grading scale |
Type of grading | Internal examination |
Criteria of assessment | The criteria of assessment are stated in the Examination
Policies and Procedures |
Additional information
Elective course
On this semester one course must be chosen out of three elective
courses (total: 5 ECTS).