GMPE340 Kalman Filter and Sensor Fusion
Showing course contents for the educational year 2022 - 2023 .
Course responsible: Jon Glenn Omholt Gjevestad
ECTS credits: 5
Faculty: Faculty of Science and Technology
Teaching language: EN, NO
Teaching exam periods:
This course has teaching/evaluation in autumn parallel.
Course frequency: Annually
First time: Study year 2020-2021
Introduction to stochastic processes and applied Kalman filtering with focus on positioning, navigation and timing applications (PNT).
Students are to understand the basic filter derivation applied to dynamic systems. This is followed by various approaches to the basic theory such as: information filter, particle filter, Bayesian estimation, relationship to least squares (LSQ) and other estimators, smoothing and methods for dealing with non-linearities.
Lectures, colloquium, student presentations, exercises.
Teaching support will be given primarily in connection with that part of the structured teaching that is set aside for exercise guidance. It will also be possible to communicate directly with the subject teacher by appointment during office hours.
Brown, R., Hwang, P. - Random Signals and Applied Kalman Filtering 4th edition
Calculus and linear algebra. Differential equations. Parameter estimation.
Good programming skills (Python / MATLAB)
Exercises. Compulsory, submitted work must be passed in order for the candidate to gain access to the exam.
Special requirements in Science
Type of course:
Lectures and discussion groups: 26 hours. Exercises: 52 hours.
The course is recommended for students in Geomatics and Robotics.
The sensor assess the exam exercises. The sensor assesses the exam for all candidates every 3 years.
Allowed examination aids: C2 Alle typer kalkulatorer, alle andre skriftlige hjelpemidler.
Examination details: Written exam: Letter grades