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Area of Science:

  • * Navigation Systems
  • * Inertial Navigation
  • * Sensor Fusion

Background:

  • * Urban indoor environments present challenges for accurate pedestrian navigation due to magnetic interference from electronic devices and ferromagnetic materials.
  • * Traditional heading calculation methods relying on geomagnetic fields are unreliable in such settings.
  • * Smartphone positioning requires robust algorithms independent of external magnetic field data.

Purpose of the Study:

  • * To develop an indoor inertial heading correction algorithm for smartphones that does not rely on magnetic field data.
  • * To enhance pedestrian smartphone positioning accuracy by addressing heading divergence.
  • * To create an autonomous system capable of judging smartphone usage modes and compensating for navigation errors.

Main Methods:

  • * Utilized the Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU) embedded in smartphones.
  • * Implemented a Gravity Assisted (GA) method to determine smartphone usage modes via gravity sensor data.
  • * Developed a Middle Time Simulated-Zero Velocity Update (MTS-ZUPT) algorithm, adapting Zero Velocity Update (ZUPT) for handheld devices, incorporating Kalman Filtering to mitigate heading drift.

Main Results:

  • * The MTS-ZUPT algorithm effectively controlled heading error diffusion without geomagnetic assistance.
  • * Integration of MTS-ZUPT with the GA method enabled autonomous usage-mode detection and heading error compensation.
  • * Achieved significant improvements in pedestrian positioning accuracy, with walking errors ranging from 1.4% to 2.0% of the total walking distance.

Conclusions:

  • * The proposed inertial navigation algorithm provides a viable solution for accurate indoor pedestrian positioning.
  • * The combined GA and MTS-ZUPT methods offer an autonomous and robust navigation system for smartphones.
  • * This approach significantly enhances navigation reliability in magnetically disturbed indoor environments.