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Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning.

Jijun Geng1, Linyuan Xia1, Jingchao Xia2

  • 1Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, 135 # Xingangxi Road, Guangzhou 510275, China.

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Summary
This summary is machine-generated.

This study introduces a smartphone-based 3D indoor positioning system using micro-electro-mechanical systems (MEMS) sensors. The method enhances pedestrian dead reckoning (PDR) accuracy for location-based services (LBS).

Keywords:
16-wind rose map3D indoor positioning methodindoor localizationrobust adaptive Kalman filterrobust adaptive cubature Kalman filter

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Area of Science:

  • Sensor fusion for indoor navigation
  • Mobile positioning systems
  • Kalman filtering applications

Background:

  • Growing demand for accurate indoor localization using smartphones.
  • Limitations of existing pedestrian dead reckoning (PDR) methods in complex indoor environments.
  • Need for precise 3D positioning including height estimation.

Purpose of the Study:

  • To develop a robust 3D indoor positioning method using smartphone MEMS sensors.
  • To improve the accuracy of pedestrian dead reckoning (PDR) for location-based services (LBS).
  • To achieve centimeter-level height estimation for enhanced indoor navigation.

Main Methods:

  • Utilizing a quaternion-based robust adaptive cubature Kalman filter (RACKF) for heading estimation from MARG sensors.
  • Distinguishing pedestrian behavior patterns (e.g., walking, stairs) using sensor data.
  • Integrating geometric building information and differential barometric altimetry for height calculation.
  • Employing a 16-wind rose map strategy for heading correction and error reduction.

Main Results:

  • Achieved Root Mean Squared Error (RMSE) for location errors between 1.04-1.65 m.
  • Obtained RMSE for height estimation errors between 0.17-0.27 m.
  • Demonstrated effective floor perception through accurate height estimation.

Conclusions:

  • The proposed 3D indoor positioning method significantly enhances accuracy compared to traditional PDR.
  • The algorithm effectively integrates sensor data and map information for precise indoor navigation.
  • The system meets the demands for intelligent user terminals in location-based services.