Bayesian bootstrap filtering for the satellite attitude determination using a star sensor

We propose a new satellite attitude determination method based on the Bayesian bootstrap filtering approach. The proposed method estimates three Euler angles using the vector observations obtained from a star sensor and using the information of gyro angular rates. The system dynamics and the measurement models of this problem are highly nonlinear functions of the Euler angles and the angular velocities. Moreover, the well-known singularity problem (of the Euler angles) may be encountered during random spinning of tile satellite. To verify the proposed method, simulation is performed and the result demonstrates that our method gives better Euler angles estimate than the EKF (extended Kalman filter).