Attitude Estimation Based on Modified Two-stage EKF

In this paper, an attitude estimation algorithm based on modified two-stage Extended Kalman Filter (EKF) is presented to improve the accuracy of attitude estimation based on Micro Electro-Mechanical System (MEMS) motion capture technology. Firstly, a two-stage EKF is presented to replace the conventional EKF, to reduce the computational complexity and computing time. Secondly, the normalized quaternion model is applied instead of Euler Angle model to reduce the calculation error of attitude estimation. Finally, the automatic error compensation realized through constructing the acceleration error covariance operator to adjust error covariance matrix according to the measurement values of the acceleration. The experiment result shows that the proposed method can significantly reduce the computational complexity and the size of universal joint deadlocks and linear acceleration effect on the attitude estimation.