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An Infrastructure-Free Indoor Localization Algorithm for Smartphones.

Qu Wang1, Haiyong Luo2, Aidong Men3

  • 1School of Information and Communication Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China. wangqu@ict.ac.cn.

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|October 5, 2018
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Summary
This summary is machine-generated.

This study introduces LiMag, an infrastructure-free indoor positioning system using magnetic fields and ambient light. It achieves accurate tracking without relying on external hardware, improving location-based services.

Keywords:
fingerprints matchingindoor positioningmagnetic fieldsmartphonevisible light

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Geomatics Engineering

Background:

  • Existing indoor localization methods (Wi-Fi, Bluetooth) require extensive infrastructure, limiting scalability and applicability.
  • There is a need for infrastructure-free indoor positioning solutions for diverse applications.
  • Magnetic fields and ambient light offer potential as ubiquitous, unmodulated signals for localization.

Purpose of the Study:

  • To develop and evaluate LiMag, an infrastructure-free indoor positioning and tracking approach.
  • To investigate the feasibility and challenges of using magnetic field and ambient light intensity for localization.
  • To enhance localization accuracy and robustness in complex indoor environments.

Main Methods:

  • Proposed LiMag, an infrastructure-free approach using magnetic fields and ambient light.
  • Developed a hybrid observation model combining magnetic and light signals.
  • Implemented single-step and long trajectory fingerprinting for improved location differentiation.
  • Utilized a particle filter framework and a long trajectory calibration scheme for accurate tracking.
  • Employed an undirected weighted graph model to reduce computational overhead.

Main Results:

  • Achieved 75th percentile localization accuracy of 1.8 m in offices and 2.2 m in shopping malls.
  • Demonstrated effectiveness in diverse scenarios, including large open-plan areas and environments with strong sunlight.
  • Validated the robustness and improved coverage of the LiMag algorithm.
  • Showcased the superiority of long trajectory fingerprints over single-step fingerprints for location differentiation.

Conclusions:

  • LiMag offers a robust, infrastructure-free indoor positioning solution.
  • The approach significantly improves localization accuracy and coverage compared to existing methods.
  • LiMag provides a viable alternative for location-based services in complex indoor environments.