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Deep Neural Networks for Indoor Geomagnetic Field Fingerprinting with Regression Approach

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Proceedings of the 22nd Engineering Applications of Neural Networks Conference (EANN 2021)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 3))

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Abstract

Geomagnetic field fingerprinting is an attractive alternative to WiFi and Bluetooth fingerprinting since the magnetic field is omnipresent and independent of any infrastructure. Recently, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been extensively used to provide fingerprinting solutions for indoor positioning based on magnetic data. Yet, no fairly comparative study has been conducted to determine which type of networks is more likely to recognize magnetic sequence patterns. In this study, we propose a CNN using Recurrence Plots (RPs) as sequence fingerprints as well as advanced RNNs treating magnetic sequences as fingerprints. We used the same real-world data in an indoor environment to build and fairly evaluate the proposed systems. Our findings show that RNNs clearly outperform the RP-based CNN, yet results in higher prediction latencies. Overall, promising positioning performances and smooth trajectory estimates are achieved for pedestrian path tracking due to approaching the indoor localization problem from a regression perspective.

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Authors and Affiliations

  1. MIS Department, Orange Labs, Grenoble, France

    Mahdi Abid & Grégoire Lefebvre

Authors
  1. Mahdi Abid
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  2. Grégoire Lefebvre
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Corresponding author

Correspondence to Grégoire Lefebvre .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Faculty of Applied Sciences, University of Sunderland, Sunderland, UK

    John Macintyre

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Abid, M., Lefebvre, G. (2021). Deep Neural Networks for Indoor Geomagnetic Field Fingerprinting with Regression Approach. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_15

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