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Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples.

Junhong Lin1, Bang Wang2, Guang Yang3

  • 1School of Electronic Information and Communications, Huazhong University of Science and Technology (HUST), Wuhan 430074, China. junhonghust@hust.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a novel algorithm to improve indoor localization accuracy using crowdsourced data. It effectively handles inaccurate location data and uneven sample distribution for reliable fingerprint database construction.

Keywords:
fingerprinting localizationsample crowdsourcingsample weightingsurface fitting

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Area of Science:

  • Computer Science
  • Signal Processing
  • Geographic Information Systems

Background:

  • Fingerprinting-based indoor localization requires extensive site surveys, which are time-consuming and labor-intensive.
  • Crowdsourcing offers a potential solution for building fingerprint databases using casually collected samples.
  • A key challenge is the unreliability of crowdsourced samples due to potential location annotation errors.

Purpose of the Study:

  • To develop a robust method for constructing indoor fingerprint databases from potentially erroneous crowdsourced data.
  • To improve the accuracy and reliability of fingerprinting-based indoor localization systems.
  • To address the challenges of sample annotation errors and nonuniform sample density.

Main Methods:

  • A cross-domain cluster intersection algorithm is proposed to assess and weight the reliability of individual crowdsourced samples.
  • High-reliability samples are selected to construct radio propagation surfaces using polynomial function fitting.
  • An entropy-like measure is utilized to weight the constructed surfaces based on subarea consistency and location discrimination.

Main Results:

  • The proposed scheme effectively addresses challenges posed by sample annotation errors and nonuniform sample density.
  • Experimental results demonstrate high localization accuracy achieved by the developed method.
  • The weighting mechanisms for samples and surfaces contribute to improved database quality.

Conclusions:

  • The developed algorithm enhances the reliability of crowdsourced data for indoor localization.
  • This approach offers a practical solution for building accurate fingerprint databases efficiently.
  • The method shows significant potential for advancing the field of indoor positioning systems.