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An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model
Qigao Fan1, Hai Zhang2, Yan Sun3
1College of Internet of Things Engineering, Jiangnan University, Wuxi 214000, China. qgfan@jiangnan.edu.cn.
Sensors (Basel, Switzerland)
|May 5, 2018
Summary
This study introduces an Optimal Enhanced Kalman Filter (OEKF) to improve pedestrian indoor positioning accuracy. The new method significantly reduces cumulative errors from inertial navigation systems (INS), achieving precise results.
Area of Science:
- Robotics
- Navigation Systems
- Sensor Fusion
Background:
- Single Inertial Navigation Systems (INS) suffer from cumulative errors and low positioning accuracy.
- Accurate pedestrian positioning in enclosed environments remains a challenge.
Purpose of the Study:
- To propose an Optimal Enhanced Kalman Filter (OEKF) for accurate pedestrian indoor positioning.
- To address and correct cumulative errors in INS.
Main Methods:
- Analysis, modeling, and reconstruction of inertial sensor errors.
- Application of attitude fusion filtering and Zero Velocity Update (ZUPT) algorithms.
- Detailed description and implementation of the OEKF algorithm.
Main Results:
- Experimental verification using a pedestrian indoor positioning platform.
- Achieved a high positioning accuracy of 0.243 m.
- Demonstrated significant improvement over traditional INS.
Conclusions:
- The OEKF effectively overcomes cumulative errors and enhances positioning accuracy.
- The proposed system offers high practical value for pedestrian indoor navigation.
- OEKF provides a robust solution for precise indoor localization.
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