A novel adaptive Kalman filter for Euler-angle-based MEMS IMU/magnetometer attitude estimation

This paper introduces a novel Euler-angle-based adaptive attitude filter for micro-electro-mechanical system inertial measurement unit (IMU)/magnetometer fusion. An intelligent coordinate switch algorithm is proposed to overcome the singularity problem that exists in the Euler-angle-based attitude calculation approach. This method rotates the physical IMU body frame and forms a quasi-computational coordinate to perform the attitude and filter update. In addition, a novel noise estimation theorem based on redundant measurement systems is introduced, proofed and employed to adaptively tune the measurement covariance matrix R in the filter. This algorithm uses gyroscope short-term accuracy to evaluate the attitude performance derived from different sensors and optimally assigns weight in the filter update. Simulated and practical experiments are carried out to test the validation of the proposed adaptive attitude filter. The results achieved demonstrate that this approach can provide a promising orientation solution.