AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Astrodynamics Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article

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

Hao Peng, Xiaoli Bai( )
Department of Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
Show Author Information

Graphical 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 leastlikely 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.

Keywords

machine learning (ML)support vector regressionartificial neural network (ANN)resident space objects (RSOs)orbit predictionGaussian processes (GPs)

References

[1]
Kelso, T. S. Iridium 33/Cosmos 2251 Collision. Information on http://celestrak.com/events/collision/ (cited 15 Feb 2017).
[2]
Abu-Mostafa, Y. S., Magdon-Ismail, M., Lin, H. T. Learning from Data. AMLBook, 2012.
[3]
Hartikainen, J., Seppänen, M., Särkkä, S. State-space inference for non-linear latent force models with application to satellite orbit prediction. In: Proceedings of the 29th International Conference on Machine Learning, 2012.
[4]
Ampatzis, C., Izzo, D. Machine learning techniques for approximation of objective functions in trajectory optimisation. In: Proceedings of the International Joint Conference on Artificial Intelligence 2009, Workshop on Artificial Intelligence in Space, 2009.
[5]
Sharma, S., Cutler, J. W. Robust orbit determination and classification: a learning theoretic approach. The Interplanetary Network Progress Report 42-203, 2015.
[6]
Barton, K. E., McLaughlin, C. A. Long short-term memory neural networks for the prediction of localized atmospheric density for orbit determination. In: Proceedings of 2018 AAS/AIAA Astrodynamics Specialist Conference, 2018.
[7]
Ertl, C. A., Christian, J. A. Identification of partially resolved objects in space imagery with neural networks. In: Proceedings of 2018 AAS/AIAA Astrodynamics Specialist Conference, 2018.
[8]
Parrish, N. L., Scheeres, D. J. Optimal low-thrust trajectory correction with neural networks. In: Proceedings of 2018 AAS/AIAA Astrodynamics Specialist Conference, 2018.
[9]
Smet, S. D., Scheeres, D. J., Parker, J. S. Systematic exploration of solar gravity driven orbital transfers in the Martian system using artificial neural networks. In: Proceedings of 2018 AAS/AIAA Astrodynamics Specialist Conference, 2018.
[10]
Jia, S. Y., Shan, J. J.Neural network-based adaptive sliding mode control for gyroelastic body. IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(3): 1519-1527.
Crossref Google Scholar
[11]
Xia, X., Jia, Y., Xu, S,.,Wang, X. An adaptive nonsingular terminal sliding mode tracking control using neural networks for space manipulators actuated by CMGs. In: Proceedings of 2018 AAS/AIAA Astrodynamics Specialist Conference, 2018.
[12]
Peng, H., Bai, X. L.Improving orbit prediction accuracy through supervised machine learning. Advances in Space Research, 2018, 61(10): 2628-2646.
Crossref Google Scholar
[13]
Peng, H., Bai, X. L.Exploring capability of support vector machine for improving satellite orbit prediction accuracy. Journal of Aerospace Information Systems, 2018, 15(6): 366-381.
Crossref Google Scholar
[14]
Peng, H., Bai, X. L.Artificial neural network-based machine learning approach to improve orbit prediction accuracy. Journal of Spacecraft and Rockets, 2018, 55(5): 1248-1260.
Crossref Google Scholar
[15]
Peng, H., Bai, X. L.Gaussian Processes for improving orbit prediction accuracy. Acta Asstronautica, 2019, 161: 44-56.
Crossref Google Scholar
[16]
Peng, H., Bai, X. L.Machine learning approach to improve satellite orbit prediction accuracy using publicly available data. The Journal of the Astronautical Sciences, 2019. .
Crossref Google Scholar
[17]
Maisonobe, L., Pommier, V., Parraud, P. Orekit: An open source library for operational flight dynamics applications. In: Proceedings of the 4th International Conference on Astrodynamics Tools and Techniques, 2010.
[18]
Vapnik, V. N. The Nature of Statistical Learning Theory, 2nd edn. New York: Springer-Verlag New York, 2000.
Crossref
[19]
Abu-Mostafa, Y., Magdon-Ismail, M., Lin, H. E. Chapter 8: Support Vector Machines. In: Learning from Data. AMLBook, 2012.
[20]
Steinwart, I., Christmann, A. Support Vector Machines. New York: Springer-Verlag New York, 2008.
Crossref
[21]
Abu-Mostafa, Y., Magdon-Ismail, M., Lin, H. E. Chapter 7: Neural networks. In: Learning from Data. AMLBook, 2012.
[22]
Nielsen, M. A. Neural Networks and Deep Learning. Determination Press, 2015.
[23]
Du, K. L., Swamy, M. N. S. Neural Networks and Statistical Learning. London: Springer-Verlag London, 2014.
[24]
Murphy, K. P. Machine Learning: a Probabilistic Perspective. Cambridge: The MIT Press, 2012.
[25]
Almosallam, I. A., Jarvis, M. J., Roberts, S. J.GPz: Non-stationary sparse Gaussian processes for heteroscedastic uncertainty estimation in photometric redshifts. Monthly Notices of the Royal Astronomical Society, 2016, 462(1): 726-739.
Crossref Google Scholar
[26]
Almosallam, I. Heteroscedastic Gaussian processes for uncertain and incomplete data. Ph.D. Dissertation. Oxford, Oxfordshire: University of Oxford, 2017.
[27]
Quiñonero-Candela, J., Rasmussen, C. E. A unifying view of sparse approximate Gaussian process regression. Journal of Machine Learning Research, 2005, 6: 1939-1959.
Google Scholar
[28]
Christopher, M. B. Pattern Recognition and Machine Learning. New York: Springer-Verlag New York, 2006.
[29]
Rasmussen, C. E., Williams, C. K. I. Gaussian Processes for Machine Learning. Cambridge: The MIT Press, 2005.
Crossref
[30]
Peng, H., Bai, X. L. Generalization capability of machine learning approach among different satellites: validated using TLE data. In: Proceedings of 2018 AAS/AIAA Astrodynamics Specialist Conference, 2018.
Astrodynamics
Volume 3 Issue 4,
December 2019
Pages 325-343
DOI: 10.1007/s42064-018-0055-4
Cite this article:
Peng H, Bai X. Comparative evaluation of three machine learning algorithms on improving orbit prediction accuracy. Astrodynamics, 2019, 3(4): 325-343. https://doi.org/10.1007/s42064-018-0055-4

695

Views

33

Crossref

29

Web of Science

43

Scopus

3

CSCD

Altmetrics
Received: 26 October 2018
Accepted: 18 April 2019
Published: 16 August 2019
© Tsinghua University Press 2019
Return