Multi-sensor covariance intersection fusion attitude estimation with unknown noise characteristics

This paper presents a robust covariance intersection(CI) fusion algorithm for the unknown covariance in the process of multi-sensor spacecraft attitude estimation with unknown noise characteristics. Firstly, the cubature Kalman filter(CKF) is used in the algorithm to get the local estimation information. Then, the local estimation information weights are obtained based on the principle of minimized non-linear performance. Finally, the CI algorithm is used to fuse the local estimation information. In addition, a method of switching between error quaternion and error generalized Rodrigues parameter is used to instead the quaternion which is redundancy to describe the spacecraft attitude. The simulation result shows the effectiveness of the proposed attitude fusion algorithm.