Examination arrangement

Course content

The course is given every second year, next time Fall 2018.

Guidance and navigation system for ships, aircraft and unmanned vehicles including AUV, UAV and USV systems. Path planning (Dubbins, clothoids, etc.), collision avoidance and classical guidance principles. Estimator-based navigation with focus on Kalman filtering and nonlinear methods (Lyapunov methods) for integration of satellite and inertial navigation systems (INS). Loosely and tightly integrated INS. Attitude estimation and representation of different estimators on SO(3) using nonlinear observer theory and the multiplicative extended Kalman filter (MEKF).

Learning outcome

KNOWLEDGE: In-depth knowledge of design and analysis of state estimators for navigation systems. Focus is placed on inertial navigation systems and aiding techniques. Guidance and navigation systems for ship, aircraft and unmanned vehicles. Knowledge of inertial sensors and global navigation systems.

SKILLS: Be able to model, simulate and implement navigation systems for unmanned underwater vehicles and aerial vehicles, ships, aircraft and satellites. Understand how Kalman filters and nonlinear observers are used to estimate position, velocity and attitude of moving objects.

GENERAL COMPETENCE: Skills in applying this knowledge and proficiency in new areas and complete advanced tasks and projects. Skills in communicating extensive independent work, and master the technical terms of nonlinear observer theory. Ability to contribute to innovative thinking and innovation processes.

Learning methods and activities

Lectures, study groups and independent study. Mandatory computer assignments. Oral examination. Th re-sit examination is also oral. The exam is only given in English.

Recommended previous knowledge

Background in nonlinear systems, stability theory, kinematics, vehicle dynamics and Kalman filtering.

Required previous knowledge

TTK 4150 Nonlinear Systems, TTK4115 Linear System Theory and TTK 4190 Guidance and Control of Vehicles or similar background.

Course materials

Lecture notes, conference and journal papers.