Autonomous Navigation for Spacecraft via Unscented Kalman Filter and Gauss-Markov Process

Abstract

Autonomous navigation can reduce the operation cost of the space missions. This paper presents an autonomous optical navigation algorithm for spacecraft on the Earth-Moon transfer trajectory via unscented Kalman filter and Gauss-Markov process. For the indeterminate external environment, the first-order Gauss-Markov process is used to estimate the unmodeled acceleration to compensate for the effects of model errors. In order to solve the nonlinear problem of the observation and state equations, the position, the velocity of spacecraft and the unmodeled acceleration are estimated by unscented Kalman filter. The strategy and the effectiveness of the algorithm are tested by numerical simulation. The results show that the algorithm can improve the navigation precision and reliability.