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Axes Mapping and Sensor Fusion for Attitude-Unconstrained Pedestrian Dead Reckoning.

Constantina Isaia1, Lingming Yu1,2, Wenyu Cai2

  • 1Department of Electrical Engineering, and Computer Science and Engineering, Cyprus University of Technology, Limassol 3036, Cyprus.

Sensors (Basel, Switzerland)
|March 28, 2026
PubMed
Summary

This study introduces a new framework for indoor pedestrian navigation using smartphones. The system enhances accuracy by fusing inertial sensor data and Wi-Fi fingerprinting, overcoming challenges with device placement and sensor noise.

Keywords:
Wi-Fi fingerprintingattitude-unconstrainedaxis mappingheading estimationpedestrian dead reckoning (PDR)sensor fusionsmartphone sensorsstep detection

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Area of Science:

  • * Indoor localization and navigation systems.
  • * Pervasive computing and ubiquitous sensing.

Background:

  • * Accurate indoor pedestrian navigation is crucial but challenging due to infrastructure limitations.
  • * Smartphone adoption necessitates robust pedestrian dead reckoning (PDR) in infrastructure-less environments.
  • * PDR accuracy is hampered by varying device orientations and noisy inertial measurement unit (IMU) data.

Purpose of the Study:

  • * To present a comprehensive PDR framework for accurate indoor navigation.
  • * To improve step counting and heading estimation for PDR systems.
  • * To mitigate cumulative errors in PDR using Wi-Fi fingerprinting.

Main Methods:

  • * Developed a robust step counting algorithm fusing raw IMU data (accelerometer, gyroscope, magnetometer).
  • * Proposed the Heading Estimation Axis Mapping (HEAT-MAP) algorithm for dynamic sensor axis adjustment.
  • * Integrated an adaptive weighted fusion mechanism with Wi-Fi fingerprinting to correct cumulative errors.

Main Results:

  • * The integrated system significantly enhances overall trajectory accuracy for indoor navigation.
  • * Achieved a high-precision, attitude-unconstrained solution for real-time PDR.
  • * Demonstrated improved robustness against varying smartphone placements and walking conditions.

Conclusions:

  • * The proposed PDR framework offers a significant advancement in indoor navigation accuracy.
  • * The fusion of IMU data, HEAT-MAP algorithm, and Wi-Fi fingerprinting effectively addresses key PDR challenges.
  • * This approach provides a reliable and precise solution for real-time indoor pedestrian navigation using smartphones.