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An Infrastructure-Free Magnetic-Based Indoor Positioning System with Deep Learning

 
: Fernandes, L.; Santos, S.; Barandas, M.; Folgado, D.; Leonardo, R.; Santos, R.; Carreiro, A.; Gamboa, H.

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Volltext ()

Sensors. Online journal 20 (2020), Nr.22, Art. 6664, 19 S.
https://www.mdpi.com/journal/sensors
ISSN: 1424-8220
ISSN: 1424-8239
ISSN: 1424-3210
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer AICOS ()

Abstract
Infrastructure-free Indoor Positioning Systems (IPS) are becoming popular due to their scalability and a wide range of applications. Such systems often rely on deployed Wi-Fi networks. However, their usability may be compromised, either due to scanning restrictions from recent Android versions or the proliferation of 5G technology. This raises the need for new infrastructure-free IPS independent of Wi-Fi networks. In this paper, we propose the use of magnetic field data for IPS, through Deep Neural Networks (DNN). Firstly, a dataset of human indoor trajectories was collected with different smartphones. Afterwards, a magnetic fingerprint was constructed and relevant features were extracted to train a DNN that returns a probability map of a user’s location. Finally, two postprocessing methods were applied to obtain the most probable location regions. We asserted the performance of our solution against a test dataset, which produced a Success Rate of around 80%. We believe that these results are competitive for an IPS based on a single sensing source. Moreover, the magnetic field can be used as an additional information layer to increase the robustness and redundancy of current multi-source IPS.

: http://publica.fraunhofer.de/dokumente/N-639745.html