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The state estimation for relative motion with respect to non-cooperative spacecraft in rendezvous and docking(RVD) is a challenging problem. In this paper, a completely non-cooperative case is considered, which means that both orbit elements and inertial tensor of target spacecraft are unknown. By formulating the equations of relative translational dynamics in the orbital plane of chaser spacecraft, the issue of unknown orbit elements is solved. And for the problem for unknown inertial tensor, we propose a novel robust estimator named interaction cubature Kalman filter(In CKF) to handle it. The novel filter consists of multiple concurrent CKFs interlacing with a maximum a posteriori(MAP) estimator. The initial estimations provided by the multiple CKFs are used in a Bayesian framework to form description of posteriori probability about inertial tensor and the MAP estimator is applied to giving the optimal estimation. By exploiting special property of spherical-radial(SR) rule, a novel method with respect to approximating the likelihood probability of inertial tensor is presented. In addition, the issue about vision sensor’s location inconformity with center mass of chaser spacecraft is also considered. The performance of this filter is demonstrated by the estimation problem of RVD at the final phase. And the simulation results show that the performance of In CKF is better than that of extended Kalman filter(EKF) and the estimation accuracy of pose and attitude is relatively high even in the completely non-cooperative case.
The state estimation for relative motion with respect to non-cooperative spacecraft in rendezvous and docking (RVD) is a challenging problem. Which means that both orbit elements and inertial tensor of target spacecraft are unknown. By formulating the equations of relative translational dynamics in the orbital plane of chaser spacecraft, the issue of unknown orbit elements is solved. And for the problem for unknown inertial tensor, we propose a novel robust estimator named interaction cubature Kalman filter (In The novel estimations provided by the maximum estimate of the posteriori (MAP) estimator. The initial estimations provided by the multiple CKFs are used in a Bayesian framework to form description of posteriori probability about inertial tensor and the MAP estimator is applied to give the optimal estimation. By exploiting special property of spherical-radial (SR) rule, a novel m ethod with respect to approximating the likelihood of of inertial tensor is presented. In addition, the issue about vision sensor’s location inconformity with center mass of chaser spacecraft is also considered. The performance of this filter is demonstrated by the estimation problem of RVD at the final And the simulation results show that the performance of In CKF is better than that of extended Kalman filter (EKF) and the estimation accuracy of pose and attitude is relatively high even in the completely non-cooperative case.