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.

Written exam

125 hours.

Special requirements in Science

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.