Variable-State-Dimension Kalman-based Filter for orientation determination using inertial and magnetic sensors

Area of Science:

• Robotics and Control Systems
• Sensor Fusion and Navigation
• State Estimation Algorithms

Background:

• Accurate three-dimensional orientation estimation is crucial for autonomous systems.
• Inertial Measurement Units (IMUs) and magnetic sensors are commonly used for this purpose.
• Traditional Extended Kalman Filters (EKFs) can struggle with dynamic environmental factors like magnetic disturbances and sensor biases.

Purpose of the Study:

• To develop a novel quaternion-based Variable-State-Dimension Extended Kalman Filter (VSD-EKF).
• To enhance orientation estimation accuracy by dynamically modeling and compensating for gyro bias and magnetic disturbances.
• To improve the robustness of orientation estimation in challenging environments.

Main Methods:

• Development of a quaternion-based VSD-EKF algorithm.
• Integration of measurements from an IMU and a triaxial magnetic sensor.
• Modeling gyro bias and magnetic disturbances within the filter's state vector.
• Implementing a switching mechanism between a quiescent EKF (GM-1) and a higher-order EKF (GM-2) for magnetic disturbance modeling.

Main Results:

• The VSD-EKF demonstrated superior performance compared to individual quiescent or higher-order EKF configurations.
• Effective compensation for gyro bias and dynamic magnetic disturbances was achieved.
• Experimental validation confirmed the algorithm's effectiveness in real-world scenarios.

Conclusions:

• The proposed VSD-EKF offers a significant advancement in 3D orientation estimation.
• Dynamic modeling and compensation of disturbances enhance filter accuracy and robustness.
• This approach provides a more reliable solution for navigation and control applications.