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Collaborative WiFi Fingerprinting Using Sensor-Based Navigation on Smartphones.

Peng Zhang1, Qile Zhao2, You Li3,4

  • 1GNSS Research Center, Wuhan University, No.129 Luoyu Road, Wuhan 430079, China. fenix@whu.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a novel method for training WiFi fingerprinting databases using sensor navigation. It demonstrates that WiFi fingerprinting errors do not linearly increase with database errors, improving indoor positioning accuracy.

Keywords:
MEMS sensorsPDRWiFiindoor positioningtraining

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Area of Science:

  • Indoor positioning systems
  • Wireless sensor networks
  • Data fusion

Background:

  • Micro-electromechanical systems (MEMS) sensors offer short-term accuracy but degrade over time for indoor navigation.
  • Existing WiFi fingerprinting methods can be sensitive to database shifts and errors.
  • Accurate indoor navigation is crucial for various applications, including robotics and augmented reality.

Purpose of the Study:

  • To develop a robust method for training WiFi fingerprinting databases using sensor-based navigation.
  • To investigate the impact of database errors on WiFi fingerprinting accuracy.
  • To enhance the reliability of indoor positioning systems.

Main Methods:

  • Utilizing sensor-based navigation solutions to train WiFi fingerprinting databases.
  • Implementing post-processing techniques to mitigate sensor accuracy degradation over time.
  • Combining multiple indoor and outdoor navigation trajectories to construct a comprehensive database.
  • Evaluating the effect of WiFi database shifts on fingerprinting performance.

Main Results:

  • The proposed method effectively trains WiFi fingerprinting databases using sensor navigation.
  • Accuracy degradation of MEMS sensors was managed by restricting trajectory lengths and applying post-processing.
  • The study found that fingerprinting errors do not increase linearly with database errors.
  • The generated database demonstrated the resilience of smartphone-based WiFi fingerprinting to database shifts.

Conclusions:

  • The developed method provides a more accurate and reliable approach to building WiFi fingerprinting databases.
  • Sensor-based navigation, when properly processed, can significantly contribute to robust indoor positioning.
  • The findings suggest that WiFi fingerprinting applications can tolerate a certain level of database error without a proportional loss in accuracy.