Research on an Improved Method for Foot-Mounted Inertial/Magnetometer Pedestrian-Positioning Based on the Adaptive...

Area of Science:

• Robotics and Autonomous Systems
• Geomatics Engineering
• Sensor Fusion

Background:

• Foot-mounted Inertial Pedestrian-Positioning Systems (FIPPSs) utilize Micro Inertial Measurement Units (MIMUs) for navigation in GNSS-denied environments.
• Zero Velocity Update (ZUPT) mitigates accumulated errors but faces challenges with fast-initial alignment and heading misalignment.
• Existing FIPPS struggle with accuracy in demanding applications like firefighting and military operations.

Purpose of the Study:

• To develop a fast-initial alignment algorithm for FIPPS using inertial and magnetometer data.
• To propose an adaptive Kalman filter-based algorithm to address unobserved heading misalignment in FIPPS.
• To enhance the accuracy and robustness of pedestrian positioning systems in challenging conditions.

Main Methods:

• Implemented an Adaptive Gradient Descent Algorithm (AGDA) for rapid initial attitude estimation and magnetic disturbance suppression.
• Developed an adaptive Kalman filter incorporating heading misalignment as an observation to compensate for magnetic disturbances.
• Introduced adaptive parameters within the Kalman filter to handle varying magnetic disturbances during pedestrian walking phases.
• Validated the proposed algorithms using pedestrian test trajectories with an MTi-G710 sensor.

Main Results:

• The AGDA effectively estimates initial attitude and improves magnetic disturbance suppression.
• The adaptive Kalman filter successfully compensates for heading misalignment, enhancing positioning accuracy.
• Experimental results demonstrate the effectiveness of the proposed algorithms in improving FIPPS performance.
• The system shows applicability for reliable pedestrian tracking in challenging environments.

Conclusions:

• The proposed AGDA and adaptive Kalman filter significantly improve the performance of foot-mounted inertial positioning systems.
• These algorithms provide robust solutions for fast-initial alignment and heading estimation, overcoming key limitations of ZUPT-based FIPPS.
• The developed methods enhance the accuracy and reliability of pedestrian navigation in environments where GNSS is unavailable or unreliable.