Abstract
In this paper, the polynomial chaos based Kriging (PC-Kriging) is utilized as a surrogate model for orbital uncertainty propagation. The polynomial chaos can represent the global trend of the uncertainty distribution whereas the Kriging captures the local uncertainty variations. A new learning strategy is proposed to incrementally build and improve the PC-Kriging model. This new PC-Kriging scheme only requires a small number of sampling points while achieving close performance to the Monte-Carlo based propagation. It is also more accurate than the random sampling based Kriging model. An orbital uncertainty propagation example is used to demonstrate the effectiveness of the proposed algorithm.
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