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 examination
ECTS	5
Permitted aids	With certain aids, see list below Unless otherwise stated in the course description in Moodle, it is permitted to bring all kinds of (engineering) aids including books, notes and advanced calculators. If the student brings a computer, it is not permitted to have access to the Internet and the teaching materials from Moodle must therefore be down loaded in advance on the computer. It is emphasized that no form of electronic communication must take place.
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	As stated in the Joint Programme Regulations. http:/​/​www.engineering.aau.dk/​uddannelse/​studieadministration/​

Additional information

Elective course
On this semester one course must be chosen out of three elective courses (total: 5 ECTS).