Single-state weighted particle filter with application to Earth Observation missions

To push the boundaries of autonomy in space, the spacecraft must rely on its own sensors to achieve positioning and environmental perception. In this context, the key problem of autonomous navigation is the nonlinear state estimation of the spacecraft in a dynamic 3D environment. In this paper, we propose a new approach based on a single-state sub-partitioning of the state vector and a partial updating of the vector of weights according to the specific information provided by each sensor. In this way, we avoid to lose information in the resampling phase thanks to a parallelization approach. The proposed method has been applied to an Earth observation mission and the efficacy of the proposed approach is demonstrated with a numerical example using a high-fidelity orbital simulator.