Abstract:In this work, a novel solution to the problem of the relative navigation during the proximity process is proposed to realize the attitude estimation and prediction of noncooperative spacecraft. It is a framework incorporating multiplicative extended Kalman filtering and attitude prediction. And the lack of angular velocity measurement and the loss of visual features are considered. By employing the augmented state vector, including inertial parameters, the attitude and relative ratios of the moment of inertia for noncooperative spacecraft are estimated in the absence of angular velocity measurement. Two attitude prediction methods, function fitting and neural network, are presented respectively. The problem of accumulating error over time of traditional methods is improved and the computational cost is reduced. The effectiveness and real-time performance are validated via numerical simulations of the proposed attitude estimation and prediction algorithms.