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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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On Fusing Wireless Fingerprints with Pedestrian Dead Reckoning to Improve Indoor Localization Accuracy.

Gimo C Fernando¹, Tinghao Qi¹, Edmund V Ndimbo¹

¹School of Information, Electronic and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.

Sensors (Basel, Switzerland)

|March 17, 2025

Summary

This study presents a new multi-source fusion positioning algorithm for accurate indoor navigation. By combining inertial sensors and signal fingerprints, it significantly improves localization accuracy compared to existing methods.

Keywords:

fusion algorithm indoor positioning inertial sensors multi-source data fusion pedestrian dead reckoning signal fingerprints

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Area of Science:

Robotics and Automation
Sensor Fusion
Indoor Navigation Systems

Background:

Single-source indoor positioning systems face challenges like signal instability and environmental obstructions.
Accurate indoor localization is crucial for applications in robotics, navigation, and Industry 4.0.

Purpose of the Study:

To develop and evaluate a multi-source fusion positioning algorithm for enhanced indoor localization.
To address the limitations of existing single-source systems by integrating diverse sensor data.

Main Methods:

The proposed algorithm fuses data from inertial sensors and wireless signal fingerprints using a weighted fusion approach.
Pedestrian dead reckoning (PDR) is employed for trajectory tracking, with its outputs integrated with signal fingerprints.

Main Results:

Experimental evaluations demonstrated significant accuracy improvements: 35.3% over wireless-only systems and 71.4% over standalone PDR.
The algorithm shows robustness in complex and dynamic indoor environments, balancing computational efficiency and accuracy.

Conclusions:

The multi-source fusion algorithm offers a robust and accurate solution for indoor positioning challenges.
This method has strong potential for practical implementation in advanced navigation, robotics, and Industry 4.0 applications.