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This study introduces an Environmental Context Indicator (ECI) for smartphone Global Navigation Satellite Systems (GNSS) to assess location quality. The ECI effectively quantifies GNSS observation reliability across diverse environments, from open areas to urban settings.

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Area of Science:

  • Geomatics Engineering
  • Mobile Computing
  • Signal Processing

Background:

  • Location-based services are increasingly integrated into daily life via smartphones.
  • Reliable localization from smartphone Global Navigation Satellite Systems (GNSS) is crucial but challenged by environmental variability.
  • Localization uncertainty necessitates methods to assess GNSS observation quality.

Purpose of the Study:

  • To develop an Environmental Context Indicator (ECI) for continuous and interpretable GNSS observation quality assessment.
  • To quantify the impact of environmental factors on GNSS signal reception and accuracy.
  • To provide a metric reflecting real-world GNSS performance on smartphones.

Main Methods:

  • Developed an ECI using carrier-to-noise density ratio (C/N0), visible satellite count, and positional dilution of precision (PDOP).
  • Utilized a Samsung Galaxy S21+ to collect data from GPS (L1/L5), Galileo (E1/E5), and BeiDou (B1) satellite signals.
  • Constructed the ECI with real-valued (0-6), integer-valued (1-5), and reliability ratio components.

Main Results:

  • The ECI successfully reflected environmental variations and corresponding GNSS quality changes.
  • ECI values were lowest in open areas and increased towards urban and indoor environments.
  • ECI demonstrated sensitivity to building density, showing significant fluctuations in urban areas.

Conclusions:

  • The developed ECI provides a valuable tool for understanding and communicating GNSS observation quality in diverse environments.
  • ECI's performance indicates its potential for improving the reliability of location-based services.
  • Environmental context significantly influences smartphone GNSS performance, necessitating context-aware localization strategies.