Abstract
In this paper, an optimal-transport-based state estimation algorithm is applied to the debris-tracking problem, with real range data from a single ranging station. It is shown that the optimal-transport-based nonlinear data assimilation technique is more accurate, robust, and consistent than commonly used ensemble Kalman filtering, especially in astrodynamics problems where uncertainty evolves in a cylindrical coordinate system. It is further shown that the optimal-transport-based state estimation provides accurate 24 h predictions for space objects. This is critical for laser ranging stations, which may miss a pass due to inaccurate predictions. This study incorporates real observation data from the International Laser Ranging Service for two passes of the Starlette satellite.
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