Mårten Lager
Saab Kockums, Malmö / Department of Computer Science, Lund University, Sweden
Elin Anna Topp
Department of Computer Science, Lund University, Sweden
Jacek Malec
Department of Computer Science, Lund University, Sweden
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Published in: 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden
Linköping Electronic Conference Proceedings 137:1, p. 10-19
Published: 2017-05-12
ISBN: 978-91-7685-496-9
ISSN: 1650-3686 (print), 1650-3740 (online)
Many ships today rely on Global Navigation Satellite System (GNSS), for their navigation, where GPS (Global Positioning System) is the most well known. Unfortunately, the GNSS systems make the ships dependent on external systems, which can be malfunctioning, be jammed or be spoofed.
There are today some proposed techniques where, e.g. bottom depth measurements are compared with known maps using Bayesian calculations, which results in a position estimation. Both maps and navigational sensor equipment are used in these techniques , most often relying on high accuracy maps, with the accuracy of the navigational sensors being less important.
Instead of relying on high accuracy maps and low accuracy navigation sensors, this paper presents an idea of the opposite, namely using low accuracy maps, but compensating this by using high accuracy navigational sensors and fusing data from both bottom depth measurements and magnetic field measurements.
Particle filter, Autonomous Navigation, Recursive Bayesian Estimation, Sea Navigation
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