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A Multi-Node Magnetic Positioning System with a Distributed Data Acquisition Architecture
Open AccessArticle

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

1
Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal
2
Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(22), 6664; https://doi.org/10.3390/s20226664
Received: 15 September 2020 / Revised: 30 October 2020 / Accepted: 18 November 2020 / Published: 20 November 2020
(This article belongs to the Special Issue Indoor Magnetic-Based Positioning System)
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. View Full-Text
Keywords: indoor positioning systems; infrastructure-free; magnetic field; deep neural networks; smartphones; fingerprinting indoor positioning systems; infrastructure-free; magnetic field; deep neural networks; smartphones; fingerprinting
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Fernandes, L.; Santos, S.; Barandas, M.; Folgado, D.; Leonardo, R.; Santos, R.; Carreiro, A.; Gamboa, H. An Infrastructure-Free Magnetic-Based Indoor Positioning System with Deep Learning. Sensors 2020, 20, 6664.

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