RT Dissertation/Thesis
T1 Indoor positioning for smartphones without infrastructure and user adaptable
A1 Rodríguez García, Germán
K1 Indoor Location
K1 Particle Filter
K1 Machine Learning
AB Given that the classic solutions for positioning outdoors, such as GPS (Global Positioning System) or GNSS (Global Navigation Satellite System) do not work indoors, there have been emerging multiple alternatives for Indoor Location. Usually these solutions require extensive and complex installations, which involve high costs. In this thesis we present a robust indoor positioning solution for smartphones that maximizes location accuracy while minimizes the required infrastructure. We have considered two main modes of displacement: walking and in a vehicle. Our solution is robust to different users, allows them to carry the phone in different positions and allows to use the device freely while performing different daily activities, such as walking, driving , going up and down stairs, etc. We achieved that by developing a robust indoor positioning system that combines information from multiple sources such as radio frequency readings and inertial sensors.
YR 2019
FD 2019
LK http://hdl.handle.net/10347/20494
UL http://hdl.handle.net/10347/20494
LA eng
DS Minerva
RD 29 abr 2026