Accuracy Improvement of Attitude Determination Systems Using EKF-Based Error Prediction Filter and PI Controller.

Area of Science:

• Robotics and Control Systems
• Navigation and Positioning Technology
• Sensor Fusion and Signal Processing

Background:

• Accurate attitude and heading reference systems (AHRS) are critical for navigation and human body tracking.
• Low-cost microelectromechanical system (MEMS) inertial sensors require advanced algorithms for precise orientation estimation.
• Attitude estimation errors in MEMS-based AHRS can significantly impact navigation and motion capture accuracy.

Purpose of the Study:

• To propose a novel intermittent calibration technique for MEMS-based AHRS.
• To enhance the accuracy of attitude estimation in navigation and human body tracking systems.
• To address the limitations of current orientation estimation methods for low-cost MEMS sensors.

Main Methods:

• Development of an intermittent calibration technique incorporating an error prediction and compensation filter.
• Utilizing a proportional integral (PI) controller to regulate gyroscope error prediction accuracy.
• Integration and testing of the proposed algorithm with real low-cost MEMS sensors (accelerometer, gyroscope, magnetometer).
• Post-processing of static and dynamic test measurements for error compensation.

Main Results:

• Achieved approximately 35% improvement in attitude estimation accuracy.
• Demonstrated the explicit performance enhancement of the proposed method in MEMS-based AHRS.
• Validated the effectiveness of the intermittent calibration technique through experimental testing.

Conclusions:

• The proposed intermittent calibration technique significantly enhances the accuracy of MEMS-based AHRS.
• The error prediction and compensation filter effectively mitigates attitude estimation errors.
• This method offers a viable solution for improving navigation and human body tracking applications using low-cost sensors.