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Improving Fingerprint-Based Positioning by Using IEEE 802.11mc FTM/RTT Observables.

Israel Martin-Escalona1, Enrica Zola1

  • 1Network Engineering Department, Universitat Politecnica de Catalunya, 08034 Barcelona, Spain.

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Summary

Round trip time (RTT) offers more reliable wireless device positioning than received signal strength (RSS). Machine learning classification models demonstrate RTT

Keywords:
IEEE 802.11mcRSSRTTWi-Fifingerprintinglocationmachine learningpositioningpositioning errorscalabilityunder-coverage

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Area of Science:

  • Wireless communication
  • Indoor positioning systems
  • Machine learning applications

Background:

  • Received signal strength (RSS) is widely used for device location but suffers from volatility.
  • IEEE 802.11mc introduced Round Trip Time (RTT) for positioning, offering greater consistency.
  • RTT is increasingly supported by devices, enabling new location computation methods.

Purpose of the Study:

  • To investigate the feasibility of Round Trip Time (RTT) in classification-based positioning algorithms.
  • To evaluate the performance of various machine learning models using RTT for accuracy and positioning errors.
  • To address the gap in research on RTT's effectiveness with classification methods.

Main Methods:

  • Assessed the performance of multiple classification models using RTT data.
  • Evaluated positioning accuracy and errors across different access point (AP) layouts, vendors, and frequency bands.
  • Compared RTT-based positioning with traditional RSS-based methods.

Main Results:

  • RTT-based positioning consistently outperformed RSS-based methods in all scenarios.
  • RTT demonstrated superior accuracy and precision, especially with limited access points.
  • Simpler machine learning algorithms, like nearest neighbor classifiers, achieved comparable results to complex models.

Conclusions:

  • Round Trip Time (RTT) is a highly accurate and precise observable for wireless positioning using classification algorithms.
  • Machine learning, particularly simpler classifiers, effectively leverages RTT for reliable location estimation.
  • RTT presents a robust alternative to RSS for indoor positioning systems.