With regard to 3D orientation estimation based on IMMU (Inertial Magnetic Measurement Unit) signals, the yaw estimation accuracy may be significantly degraded as a result of magnetic distortions. Consequently, several yaw estimation Kalman filters (KFs) possessing distortion compensation mechanisms have been proposed. However, majority of the conventional methods fail to effectively curb inaccuracies due to distortion when magnetic fields are extremely distorted. In this paper, we propose a new KF projecting a kinematic constraint to minimize yaw estimation errors induced by magnetic distortions. After the measurement update using magnetometer signals, the proposed method additionally corrects the yaw estimation through projection of a kinematic constraint on a conventional unconstrained KF. Experimental results show that the proposed KF outperformed the conventional KF by approximately 52-67%.

Three-axis magnetometers are widely used in various fields requiring azimuth information. However, accuracy of azimuth estimation based on magnetometer signals may be degraded because of errors such as offset, scale factor, nonorthogonality, hard-iron distortion, and soft-iron distortion. Recently, several ellipsoid-fitting calibration techniques have been proposed and have received much attention. However, comparative analysis of calibration accuracies between these techniques has not been conducted. This study compared and analyzed performance of four ellipsoid-fitting magnetometer calibration techniques such as the linear least square method, the two-step algorithm, and two different nonlinear least square methods. Our analysis and experimental results reveal superiority of the linear least square method compared to other methods in terms of calibration accuracy as well as ease of use in practice.