### Modeling Orbital Propagation Using Regression Technique and Artificial Neural Network

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J. N. Pelton, “A path forward to better space security: Finding new solutions to space debris, space situational awareness and space traffic management,” J. Sp. Saf. Eng., vol. 6, no. 2, pp. 92–100, 2019.

E.-J. Choi et al., “Performance analysis of sensor systems for space situational awareness,” J. Astron. Sp. Sci., vol. 34, no. 4, pp. 303–314, 2017.

H. Peng and X. Bai, “Improving orbit prediction accuracy through supervised machine learning,” Adv. Sp. Res., vol. 61, no. 10, pp. 2628–2646, 2018.

H. Peng and X. Bai, “Exploring capability of support vector machine for improving satellite orbit prediction accuracy,” J. Aerosp. Inf. Syst., vol. 15, no. 6, pp. 366–381, 2018.

S. A. Kaiser, “Legal and policy aspects of space situational awareness,” Space Policy, vol. 31, pp. 5–12, 2015.

W. Lu et al., “Microservice-based platform for space situational awareness data analytics,” Int. J. Aerosp. Eng., vol. 2020, 2020.

“ARES | Orbital Debris Program Office | LEGEND.” .

N. Salleh, S. S. Yuhaniz, N. F. M. Azmi, and S. F. Sabri, “Enhancing simplified general perturbations-4 model for orbit propagation using deep learning: A review,” in ACM International Conference Proceeding Series, 2019, vol. Part F1479, doi: 10.1145/3316615.3316675.

J. F. San-Juan, I. Pérez, M. San-Martín, and E. P. Vergara, “Hybrid SGP4 orbit propagator,” Acta Astronaut., vol. 137, pp. 254–260, 2017.

A. Celletti, C. Efthymiopoulos, F. Gachet, C. Galeş, and G. Pucacco, “Dynamical models and the onset of chaos in space debris,” Int. J. Non. Linear. Mech., vol. 90, pp. 147–163, 2017.

T. Carrico, J. Carrico, L. Policastri, and M. Loucks, “Investigating orbital debris events using numerical methods with full force model orbit propagation,” Adv. Astronaut. Sci, vol. 130, no. PART 1, pp. 407–426, 2008.

S. Valk, A. Lemaître, and L. Anselmo, “Analytical and semi-analytical investigations of geosynchronous space debris with high area-to-mass ratios,” Adv. Sp. Res., vol. 41, no. 7, pp. 1077–1090, 2008.

Y. Sun, M. Peng, Y. Zhou, Y. Huang, and S. Mao, “Application of machine learning in wireless networks: Key techniques and open issues,” IEEE Commun. Surv. Tutorials, vol. 21, no. 4, pp. 3072–3108, 2019.

I. E. Dawoodjee and M. Rajeswari, “Establishing a regression baseline for predicting satellite motion,” J. Appl. Technol. Innov. (e-ISSN 2600-7304), vol. 5, no. 1, p. 47, 2021.

H. Peng and X. Bai, “Artificial Neural Network–Based Machine Learning Approach to Improve Orbit Prediction Accuracy,” J. Spacecr. Rockets, vol. 55, no. 5, pp. 1248–1260, 2018.

J. M. Valente and S. Maldonado, “SVR-FFS: A novel forward feature selection approach for high-frequency time series forecasting using support vector regression,” Expert Syst. Appl., vol. 160, p. 113729, 2020.

A. Khosravi, L. Machado, and R. O. Nunes, “Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil,” Appl. Energy, vol. 224, pp. 550–566, 2018.

S. Bhardwaj, E. Chandrasekhar, P. Padiyar, and V. M. Gadre, “A comparative study of wavelet-based ANN and classical techniques for geophysical time-series forecasting,” Comput. Geosci., vol. 138, p. 104461, 2020.

A. H. Nury, K. Hasan, and M. J. Bin Alam, “Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh,” J. King Saud Univ., vol. 29, no. 1, pp. 47–61, 2017.

F. Di Martino and F. Delmastro, “High-Resolution Physiological Stress Prediction Models based on Ensemble Learning and Recurrent Neural Networks,” in 2020 IEEE Symposium on Computers and Communications (ISCC), 2020, pp. 1–6.

L. Chen et al., Orbital Data Applications for Space Objects. Springer, 2017.

H. Peng and X. Bai, “Machine learning approach to improve satellite orbit prediction accuracy using publicly available data,” J. Astronaut. Sci., vol. 67, no. 2, pp. 762–793, 2020.

D. Vallado and P. Crawford, “SGP4 orbit determination,” in AIAA/AAS Astrodynamics Specialist Conference and Exhibit, 2008, p. 6770.

M. Lin, M. Xu, and X. Fu, “A parallel algorithm for the initial screening of space debris collisions prediction using the SGP4/SDP4 models and GPU acceleration,” Adv. Sp. Res., vol. 59, no. 9, pp. 2398–2406, 2017.

B. Alizadeh Savareh, A. Bashiri, A. Behmanesh, G. H. Meftahi, and B. Hatef, “Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis,” PeerJ Prepr., vol. 6, p. e27020v1, 2018.

F. R. Hoots and R. L. Roehrich, “Models for Propagation of Norad Element Sets,” Aerospace Defense Command Peterson AFB Co Office of Astrodynamics, 1980.

B. Li, J. Sang, and J. Chen, “Achievable orbit determination and prediction accuracy using short-arc space-based observations of space debris,” Adv. Sp. Res., vol. 62, no. 11, pp. 3065–3077, 2018.

J. Choi, J. H. Jo, H.-S. Yim, E.-J. Choi, S. Cho, and J.-H. Park, “Optical tracking data validation and orbit estimation for sparse observations of satellites by the OWL-Net,” Sensors, vol. 18, no. 6, p. 1868, 2018.

DOI: http://dx.doi.org/10.18517/ijaseit.12.3.15366

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