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Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning.

Ladislav Polak1, Stanislav Rozum1, Martin Slanina1

  • 1Department of Radio Electronics, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3082/12, 616 00 Brno, Czech Republic.

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This study enhances indoor positioning using Bluetooth Low Energy fingerprinting by incorporating multiple anchors and radio channels. Random Forest machine learning achieved over 99% accuracy, improving reliability in received signal strength-based localization.

Keywords:
Bluetoothfingerprintingindoor navigationmachine learning

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Machine Learning

Background:

  • Indoor positioning systems commonly use fingerprinting based on signal strength measurements.
  • Traditional methods suffer from reliability issues due to inaccurate power measurements in wireless systems.
  • Existing studies on machine learning for radio fingerprinting are difficult to compare directly.

Purpose of the Study:

  • To improve the accuracy and reliability of indoor positioning using Bluetooth Low Energy (BLE) fingerprinting.
  • To explore the impact of multiple anchors and radio channels on positioning accuracy.
  • To evaluate the performance of various machine learning algorithms for BLE-based localization.

Main Methods:

  • Utilized multiple anchors and radio channels to extend power level measurements.
  • Investigated different alignment approaches for received signal strength (RSS) measurements.
  • Implemented and analyzed four supervised machine learning techniques: k-Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network.

Main Results:

  • The Random Forest algorithm demonstrated superior performance, achieving a classification accuracy exceeding 99%.
  • The study analyzed the accuracy-complexity trade-off for candidate algorithms in 1D and 2D environments.
  • A comprehensive literature survey identified challenges in comparing existing machine learning applications in radio fingerprinting.

Conclusions:

  • Enhancing BLE fingerprinting with multiple anchors and channels, combined with machine learning, significantly improves indoor positioning accuracy.
  • Random Forest emerges as a highly promising technique for accurate and reliable BLE-based localization.
  • The findings provide valuable insights into algorithm selection based on accuracy and complexity requirements.