Abstract: In order to reduce the effect of systematic bias on the performance of stellar aberration navigation system, a systematic bias estimation and compensation method based on a parametric model and an extended Kalman filter (EKF) is proposed. The systematic bias is described uniformly as relative installation errors of sensors. A stellar aberration navigation system model is established where spacecraft motion state, measurement noise and systematic bias are taken into consideration. Then a navigation filter is designed based on the model to estimate the systematic bias parameters together with the position and velocity of the spacecraft. The feasibility of the systematic bias self-calibration method is analyzed through the calculation of the Cramer-Rao lower bound (CRLB). The numerical simulation is implemented for an Earth satellite. It shows that the unfavorable effect of the system is suppressed evidently by using the proposed method. With an inter-star angle measurement accuracy of 0.1″, after the systematic bias self-calibration, the positioning accuracy of the stellar aberration navigation system is improved from about 50km to less than 3km. In comparison with the existing approach where the systematic bias is modeled as the inter-star angle measurement bias, the main advantage of the proposed method is that the dimension of the state vector to be estimated is independent of the number of the measurement, which facilitates the design of the onboard navigation algorithms.

Key words: spacecraft, autonomous navigation, stellar aberration, systematic bias, extended Kalman filter