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An Unsupervised Learning Technique to Optimize Radio Maps for Indoor Localization.

Jens Trogh1, Wout Joseph2, Luc Martens3

  • 1Department of Information Technology, IMEC-Ghent University, Ghent 9052, Belgium. jens.trogh@ugent.be.

Sensors (Basel, Switzerland)
|February 21, 2019
PubMed
Summary

This study introduces an automated method for creating accurate radio maps for indoor positioning. It uses unsupervised learning to optimize model-based maps, achieving high accuracy with minimal unlabeled data, reducing manual effort.

Keywords:
fingerprintingindoor environmentlocalizationpositioningradio maprsstrackingunsupervised learning

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Area of Science:

  • Computer Science
  • Robotics
  • Signal Processing

Background:

  • Signal strength-based indoor positioning relies heavily on radio maps (fingerprint databases).
  • Traditional measurement-based radio maps are accurate but labor-intensive to create and maintain.
  • Model-based radio maps offer faster generation but often lack sufficient accuracy.

Purpose of the Study:

  • To propose a method for automatic construction and optimization of model-based radio maps.
  • To eliminate the need for extensive measurement campaigns or inertial sensor data.
  • To improve the accuracy of model-based radio maps for indoor positioning.

Main Methods:

  • Utilized unsupervised learning with random walks (unlabeled ground truth locations) as input.
  • Integrated a floor plan and a location tracking algorithm for map optimization.
  • Avoided manual site surveys and the use of power-consuming inertial sensors.

Main Results:

  • Achieved median accuracies of up to 2.07 m in a large office building (over 1100 m²).
  • Demonstrated a relative accuracy improvement of 28.6%.
  • Required only 15 minutes of unlabeled training data for optimization.

Conclusions:

  • The proposed method significantly enhances the accuracy of model-based radio maps.
  • Automated radio map generation reduces the burden of manual data collection for indoor positioning.
  • This approach offers a practical and efficient solution for high-accuracy indoor localization.