Two-Line Element Estimation Using Machine Learning
| Autor: | Sudantha Balage, Melrose Brown, Russell Boyce, Rasit Abay |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Two-line element set Computer science business.industry Decision tree FOS: Physical sciences Aerospace Engineering Machine learning computer.software_genre Space Physics (physics.space-ph) Test case Physics - Space Physics Space and Planetary Science Physics::Space Physics Trajectory Artificial intelligence Gradient boosting Orbit determination business Global optimization computer |
| Zdroj: | The Journal of the Astronautical Sciences. 68:273-299 |
| ISSN: | 2195-0571 0021-9142 |
| DOI: | 10.1007/s40295-021-00249-0 |
| Popis: | Two-line elements are widely used for space operations to predict orbits with a moderate accuracy for 2-3 days. Local optimization methods can estimate a TLE as long as there exists an initial estimate, whereas global optimization methods are computationally intensive, and estimating a large number of them is prohibitive. In this paper, the feasibility of predicting the initial estimates within the radius of convergence of the actual TLEs using machine learning methods is investigated. First, a Monte-Carlo approach to estimate a TLE, when there is no initial estimate that is within the radius of convergence of the actual TLE, is introduced. The proposed Monte-Carlo method is leveraged for demonstrating the behavior of the fitting error between the realistic trajectory and the trajectory propagated by SGP4 theory during the TLE estimation processes and evaluating the unbiased performance of the proposed machine learning models. Second, gradient boosting decision trees and fully-connected neural networks are trained to map the orbital evolution of space objects to the associated TLEs using 9.5 million publicly available TLEs from the US space catalog. The desired precision in the mapping to estimate a TLE is achieved for one of the three test cases, which is a low area-to-mass ratio space object. |
| Databáze: | OpenAIRE |
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