Master's Programme in Data Science is responsible for the course.
The course is available to students from other degree programmes but priority will be given to students of the degree programme(s) organising the course.
Prerequisites in terms of knowledge
No knowledge of navigation is required, but basic knowledge of Matlab will be needed
Prerequisites for students in the Data Science programme, in terms of courses
Prerequisites for other students in terms of courses
Recommended preceding courses
The student will learn the basics of navigation using Global Navigation Satellite Systems (GNSS), such as GPS signals, and other navigation technologies, also for indoor navigation.
The student will also get a hands-on training on the GNSS receiver functionalities via a course work assignment.
The course will be taught for the first time in year 2018 overlapping two periods, 2nd and 3rd teaching periods due to the synchronization with University of Vaasa's and Aalto University's schedules.
The course will give basic knowledge of different aspects related to satellite navigation and other technologies used for e.g. indoor navigation. The specific topics are:
Introduction to GNSS technologies
Next generation GNSS
Differential GNSS and SBAS
PPP and RTK
GNSS and INS
GNSS and other location technologies
Lecture notes and other additional material given during the course will provide the required knowledge.
GNSS Applications and Methods, by S. Gleason and D. Gebre-Egziabher (Eds.), Artech House Inc., 2009,
GPS: Signals, Measurements and Performance (Second Edition), by P. Misra and P. Enge, Ganga Jamuna Press, 2006, www.gpstextbook.com
Grading scale 1-5. Grading will be based on Exercises (30% of the total grade), Examination (40%), Project assignment (24%), Attendance at / Watching Lectures (6%)
The course can be taken as a distance learning course. Lectures are given and broadcasted partly from the University of Vaasa, but a teacher is present in Kumpula at all times. Lectures are recorded and may be watched any time during the course.
The course will be completed via exercises, project assignment and an exam. Extra points will be given for attending / watching the lectures in Moodle.
Associate professor Laura Ruotsalainen