Aircraft attitude estimation based on quaternion state-switching unscented Kalman filter
用四元数状态切换无迹卡尔曼滤波器估计的飞行器姿态

To deal with the divergence phenomenon of extended Kalman filter (EKF) in SINS/CCD attitude estimation, we put forward a state-switching unscented Kalman filter (UKF) based on quaternion (Q-SUKF). By switching the states in real-time, the dimensions of the full-dimension UKF algorithm are effectively reduced, the computation complexity is decreased and the timeliness is improved. To avoid the quaternion normalization limitation in UKF algorithm, a cost function based on the eigenvector resolution is derived to compute the average quaternion. To deal with the singularity of the quaternion covariance, we employ the multiplicative quaternion error to represent the distance between quaternion update points and the estimation points. The simulation results show that the proposed algorithm provides higher estimation precision than the EKF algorithm, and achieves almost the same estimation precision as the full-dimension UKF and Modified Rodrigues parameter UKF. Furthermore, this algorithm needs shorter estimation time than all other algorithms mentioned above.