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Summary

This study introduces a fast radio map construction for indoor localization using crowdsourced smartphone data and adaptive path loss modeling. The method significantly reduces construction time and effort while maintaining high positioning accuracy.

Keywords:
fingerprintingindoor positioningleast squarespath loss modelradio map

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Area of Science:

  • Wireless communication
  • Indoor localization technologies
  • Radio frequency mapping

Background:

  • Indoor fingerprinting localization relies on radio maps, which are typically time-consuming and labor-intensive to create.
  • Existing methods for radio map construction present significant challenges in terms of efficiency and scalability.

Purpose of the Study:

  • To develop a fast and efficient method for constructing radio maps for indoor fingerprinting localization.
  • To reduce the time and labor required for radio map creation while maintaining positioning accuracy.

Main Methods:

  • Utilizing crowdsourced Received Signal Strength (RSS) fingerprints collected by multiple smartphones at sparse reference points.
  • Employing an adaptive path loss model interpolation technique based on the least squares method to build AP models in small areas.
  • Estimating RSS values at interpolated points, considering RSS loss, noise, and threshold, to form interpolated fingerprints.
  • Combining interpolated and sparse reference fingerprints to generate the complete radio map.

Main Results:

  • The proposed method achieved comparable positioning accuracy to a complete manual radio map, even with sparse reference points (9.6 m interval).
  • Demonstrated a significant reduction of 85% in the efforts and time required for radio map construction.
  • Experimental results validated the effectiveness of the adaptive path loss model interpolation against inverse distance weighted and Kriging methods.

Conclusions:

  • The adaptive path loss model interpolation offers a fast and efficient solution for radio map construction in indoor localization.
  • Crowdsourcing with smartphones and intelligent interpolation can overcome the limitations of traditional labor-intensive radio map creation.
  • This approach significantly enhances the practicality and scalability of indoor fingerprinting localization systems.