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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Online joint localization without user interactions.

Yanfen Le1, Jintian Ou2, Yuanhao Chen2

  • 1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China. leyanfen@usst.edu.cn.

Scientific Reports
|December 15, 2023
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Summary
This summary is machine-generated.

This study introduces JointLoc, an online Wi-Fi fingerprinting localization scheme. JointLoc improves indoor positioning accuracy by leveraging spatial signal correlations and eliminating anomalous signals without direct user interaction.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Wi-Fi fingerprinting is a common indoor localization method.
  • Received Signal Strength (RSS) fingerprinting faces accuracy challenges due to signal fluctuations in complex environments.

Purpose of the Study:

  • To propose an online joint localization scheme (JointLoc) for robust and accurate indoor positioning.
  • To address the limitations of stand-alone and collaborative localization methods.

Main Methods:

  • JointLoc utilizes the spatial correlation of wireless signal distribution for joint localization.
  • It identifies neighboring users based on proximity without direct interaction.
  • A novel subset-based localization scheme is integrated to mitigate anomalous RSS signals.

Main Results:

  • JointLoc demonstrates robustness against signal fluctuations in real-world environments.
  • The scheme achieves good localization accuracy compared to conventional and state-of-the-art algorithms.

Conclusions:

  • JointLoc offers an effective solution for accurate and reliable indoor Wi-Fi localization.
  • The proposed method enhances positioning performance by intelligently utilizing environmental signal characteristics.