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Comparative evaluation of three machine learning algorithms on improving orbit prediction accuracy

Comparative evaluation of three machine learning algorithms on improving orbit prediction accuracy Abstract In this paper, the recently developed machine learning (ML) approach to improve orbit prediction accuracy is systematically investigated using three ML algorithms, including support vector machine (SVM), artificial neural network (ANN), and Gaussian processes (GPs). In a simulation environment consisting of orbit propagation, measurement, estimation, and prediction processes, totally 12 resident space objects (RSOs) in solar-synchronous orbit (SSO), low Earth orbit (LEO), and medium Earth orbit (MEO) are simulated to compare the performance of three ML algorithms. The results in this paper show that ANN usually has the best approximation capability but is easiest to overfit data; SVM is the least likely to overfit but the performance usually cannot surpass ANN and GPs. Additionally, the ML approach with all the three algorithms is observed to be robust with respect to the measurement noise. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Astrodynamics Springer Journals

Comparative evaluation of three machine learning algorithms on improving orbit prediction accuracy

Astrodynamics , Volume 3 (4): 19 – Dec 1, 2019

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Publisher
Springer Journals
Copyright
2019 Tsinghua University Press
ISSN
2522-008X
eISSN
2522-0098
DOI
10.1007/s42064-018-0055-4
Publisher site
See Article on Publisher Site

Abstract

Abstract In this paper, the recently developed machine learning (ML) approach to improve orbit prediction accuracy is systematically investigated using three ML algorithms, including support vector machine (SVM), artificial neural network (ANN), and Gaussian processes (GPs). In a simulation environment consisting of orbit propagation, measurement, estimation, and prediction processes, totally 12 resident space objects (RSOs) in solar-synchronous orbit (SSO), low Earth orbit (LEO), and medium Earth orbit (MEO) are simulated to compare the performance of three ML algorithms. The results in this paper show that ANN usually has the best approximation capability but is easiest to overfit data; SVM is the least likely to overfit but the performance usually cannot surpass ANN and GPs. Additionally, the ML approach with all the three algorithms is observed to be robust with respect to the measurement noise.

Journal

AstrodynamicsSpringer Journals

Published: Dec 1, 2019

References