An infrastructure-free magnetic-based indoor positioning system with deep learning

Letícia Fernandes, Marília Barandas, Duarte Folgado, Ricardo Leonardo, Ricardo Santos, André Carreiro, Hugo Gamboa

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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.

Original languageEnglish
Article number6664
Pages (from-to)1-19
Number of pages19
JournalSensors (Switzerland)
Volume20
Issue number22
DOIs
Publication statusPublished - 2 Nov 2020

Keywords

  • Deep neural networks
  • Fingerprinting
  • Indoor positioning systems
  • Infrastructure-free
  • Magnetic field
  • Smartphones

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