A UWB/Improved PDR Integration Algorithm Applied to Dynamic Indoor Positioning for Pedestrians

Area of Science:

• Robotics
• Sensor Fusion
• Human-Computer Interaction

Background:

• Inertial sensors are crucial for human motion monitoring and pedestrian positioning but suffer from data drift, impacting accuracy.
• Traditional pedestrian dead-reckoning algorithms struggle with step count and heading accuracy, limiting their application.
• Existing methods lack robust solutions for accurate and stable indoor dynamic positioning.

Purpose of the Study:

• To propose an indoor dynamic positioning method with self-correcting capabilities for human motion.
• To enhance positioning accuracy and stability by addressing data drift and signal limitations.
• To integrate inertial sensors and ultra-wideband (UWB) technology for improved performance.

Main Methods:

• Developed a novel indoor dynamic positioning method leveraging human motion's symmetrical characteristics for error self-correction.
• Implemented an ultra-wideband (UWB) positioning system integrated with inertial sensors.
• Utilized an unscented Kalman filter to fuse data from inertial sensors and UWB for comprehensive positioning.

Main Results:

• The proposed method effectively compensates for inertial sensor data drift and UWB signal obstacles.
• Fusion of inertial and UWB data significantly improves positioning accuracy and response time.
• Experimental results demonstrate high accuracy and real-time performance in static and dynamic tests.

Conclusions:

• The integrated inertial and UWB positioning system offers a robust solution for indoor dynamic positioning.
• The error self-correcting function based on human motion symmetry enhances system reliability.
• The developed system meets application requirements for accurate and stable human motion monitoring and positioning.