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Accurate WiFi-Based Indoor Positioning with Continuous Location Sampling

  • J. E. van EngelenEmail author
  • J. J. van Lier
  • F. W. Takes
  • H. Trautmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)

Abstract

The ubiquity of WiFi access points and the sharp increase in WiFi-enabled devices carried by humans have paved the way for WiFi-based indoor positioning and location analysis. Locating people in indoor environments has numerous applications in robotics, crowd control, indoor facility optimization, and automated environment mapping. However, existing WiFi-based positioning systems suffer from two major problems: (1) their accuracy and precision is limited due to inherent noise induced by indoor obstacles, and (2) they only occasionally provide location estimates, namely when a WiFi-equipped device emits a signal. To mitigate these two issues, we propose a novel Gaussian process (GP) model for WiFi signal strength measurements. It allows for simultaneous smoothing (increasing accuracy and precision of estimators) and interpolation (enabling continuous sampling of location estimates). Furthermore, simple and efficient smoothing methods for location estimates are introduced to improve localization performance in real-time settings. Experiments are conducted on two data sets from a large real-world commercial indoor retail environment. Results demonstrate that our approach provides significant improvements in terms of precision and accuracy with respect to unfiltered data. Ultimately, the GP model realizes continuous location sampling with consistently high quality location estimates.

Keywords

Indoor positioning Gaussian processes Crowd flow analysis Machine learning WiFi 

Notes

Acknowledgements

Authors acknowledge support from the European Research Center for Information Systems (ERCIS). The third author was supported by the European Research Council (ERC), EU Horizon 2020 grant agreement number 638946. Authors thank people who volunteered to generate calibration data sets.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • J. E. van Engelen
    • 1
    Email author
  • J. J. van Lier
    • 2
  • F. W. Takes
    • 1
  • H. Trautmann
    • 3
  1. 1.Department of Computer Science (LIACS)Leiden UniversityLeidenThe Netherlands
  2. 2.Big Data & Analytics, KPMG Advisory N.V.AmstelveenThe Netherlands
  3. 3.Department of Information SystemsUniversity of MünsterMünsterGermany

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