Pseudo-Linear Attitude Control and Estimation System (PLACES)

Design of small and midsize spacecraft is the focus of many future low-budget missions. Hence, the demand for an efficient and effective autonomous algorithm for attitude and rate estimation is more prominent than ever. This thesis investigates the performance of a Pseudo-Linear Kalman Filter (PLKF ) with continuous-time dynamics for attitude and rate estimation. The PLKF differs from the traditional Extended Kalman Filter (EKF) with its continuous-time dynamics performing estimation using a pseudo-linear state-dependent dynamic model. Both quaternion-based and vector-based measurements have been considered and the dynamic model of the spacecraft accounts for external environmental disturbances. The theoretical background of the PLKF will be summarized. Using computer simulations, the designed filter is shown to overcome large initial errors and yield accurate estimates. The performance indices used for evaluation are based on robustness against plant, measurement and initial estimate errors, as well as rejection of different sensor noise levels. While the EKF remains more robust for some cases of angular velocity estimation, the PLKF, in general, outperforms the EKF in attitude estimation.