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

• Published in: Sensors (Basel, Switzerland) Switzerland), 2020-11, Vol.20 (22), p.6664
• Main Authors: Fernandes, Letícia, Santos, Sara, Barandas, Marília, Folgado, Duarte, Leonardo, Ricardo, Santos, Ricardo, Carreiro, André, Gamboa, Hugo
• Format: Article
• Language: English
• Subjects: Accuracy; Architecture; Datasets; Deep learning; Infrastructure; Magnetic fields; Mobile operating systems; Neural networks; Radio frequency identification; Sensors; Smartphones
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20226664

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.