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Advanced Orbit Prediction for Resident Space Objects through Physics-based Learning

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Rutgers, The State University of New Jersey New Brunswick United States

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The goal of this research is to develop a novel methodology to predict trajectories of resident space objects RSOs with orders-of-magnitudeshigher accuracy than the current methods. We propose to enhance physics-based orbit prediction with a learning-based system identification well suited for the challenging, unstable, and inactive RSOs that are out of control and have uncertain origins. We have developed a simulation-based space catalog environment to validate the proposed orbit prediction method. For the first time, our simulation results demonstrated three types of generalization capability for the proposed approach. We have also validated the developed ML methodology using publicly available data.

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Technical Report,15 Apr 2016,14 Apr 2019



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Approved For Public Release;

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