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Wi-Fi/MARG Integration for Indoor Pedestrian Localization.
Zengshan Tian1, Yue Jin2, Mu Zhou3
1Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. tianzs@cqupt.edu.cn.
Sensors (Basel, Switzerland)
|December 16, 2016
Summary
This study introduces a novel indoor localization system using Wi-Fi and Magnetic, Angular Rate, and Gravity (MARG) sensors. The integrated system achieves superior accuracy, with an average error of 0.85 meters.
Area of Science:
- Computer Science
- Electrical Engineering
- Robotics
Background:
- Wi-Fi based indoor localization is practical but can be improved.
- Magnetic, Angular Rate, and Gravity (MARG) sensors offer high-accuracy localization.
- Integrating Wi-Fi and MARG sensors can enhance indoor positioning.
Purpose of the Study:
- To design and develop a novel indoor localization system fusing Wi-Fi and MARG sensor data.
- To enhance existing Pedestrian Dead Reckoning (PDR) and Wi-Fi localization algorithms.
- To create an Extended Kalman Particle Filter (EKPF) based fusion algorithm for improved accuracy.
Main Methods:
- Developed a new Wi-Fi/MARG indoor localization system.
- Implemented an Android mobile client, a web-based remote control interface, and a location server.
- Utilized an Extended Kalman Particle Filter (EKPF) for sensor data fusion.
Main Results:
- The proposed system demonstrated superior localization performance compared to Wi-Fi or MARG sensors alone.
- Achieved an average localization error of 0.85 meters.
- The system provides real-time indoor pedestrian localization.
Conclusions:
- The fusion of Wi-Fi and MARG sensors significantly improves indoor localization accuracy.
- The developed EKPF-based fusion algorithm is effective for real-time pedestrian positioning.
- The novel system offers a practical and accurate solution for indoor navigation.
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