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Low-Effort Place Recognition with WiFi Fingerprints Using Deep Learning

  • Michał Nowicki
  • Jan Wietrzykowski
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 550)

Abstract

Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions.

Keywords

WiFi Fingerprinting Indoor localization Deep neural networks 

Notes

Acknowledgements

This research was funded by the National Science Centre in Poland in years 2016–2019 under the grant 2015/17/N/ST6/01228.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Control and Information EngineeringPoznań University of TechnologyPoznańPoland

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