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DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization.

Marius Laska1, Jörg Blankenbach1

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Summary

DeepLocBox (DLB) improves indoor localization accuracy using Wi-Fi fingerprints. This deep learning model predicts a bounding box for user location, outperforming traditional methods in multi-floor environments.

Keywords:
deep learningfingerprintingindoor area localizationmulti-buildingmulti-floor

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Geomatics Engineering

Background:

  • Global Navigation Satellite Systems (GNSS) provide reliable outdoor positioning.
  • Indoor localization lacks a comparable standard, with Wireless Local Area Network (WLAN) fingerprinting being a common but limited approach.
  • Deep learning enhances WLAN fingerprinting for improved indoor positioning.

Purpose of the Study:

  • To introduce DeepLocBox (DLB), a novel deep learning model for indoor area localization.
  • To enable reliable localization in multi-building/multi-floor environments without pre-segmented floor plans.
  • To minimize the prediction space by having the model predict a bounding box containing the user's position.

Main Methods:

  • Developed DeepLocBox (DLB), a deep learning model for indoor localization.
  • DLB predicts a bounding box around the user's position, reducing the required prediction space.
  • Compared DLB's performance against standard neural network-based position estimation.

Main Results:

  • DLB demonstrated improved success probability across multiple datasets.
  • Achieved a 9.48% gain on a dataset from RWTH Aachen University, Germany.
  • Showed 5.48% and 3.71% gains on datasets from Tampere University of Technology (TUT), Finland, and Universitat Jaume I (UJI), Spain, respectively.

Conclusions:

  • DeepLocBox (DLB) offers a robust solution for indoor area localization in complex environments.
  • The bounding box prediction method enhances accuracy and efficiency compared to existing techniques.
  • DLB represents a significant advancement in deep learning-based indoor positioning systems.