Fusion of a machine learning approach and classical orbit predictions

no code implementations • Acta Astronautica 2021 • Hao Peng, Xiaoli Bai

Using the extended Kalman filter (EKF) as the orbit estimation and prediction method, this paper presents an innovative fusion strategy to incorporate the ML output to the conventional framework.

Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations

no code implementations • 22 Jun 2020 • Gelu Nita, Manolis Georgoulis, Irina Kitiashvili, Viacheslav Sadykov, Enrico Camporeale, Alexander Kosovichev, Haimin Wang, Vincent Oria, Jason Wang, Rafal Angryk, Berkay Aydin, Azim Ahmadzadeh, Xiaoli Bai, Timothy Bastian, Soukaina Filali Boubrahimi, Bin Chen, Alisdair Davey, Sheldon Fereira, Gregory Fleishman, Dale Gary, Andrew Gerrard, Gregory Hellbourg, Katherine Herbert, Jack Ireland, Egor Illarionov, Natsuha Kuroda, Qin Li, Chang Liu, Yuexin Liu, Hyomin Kim, Dustin Kempton, Ruizhe Ma, Petrus Martens, Ryan McGranaghan, Edward Semones, John Stefan, Andrey Stejko, Yaireska Collado-Vega, Meiqi Wang, Yan Xu, Sijie Yu

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists.

BIG-bench Machine Learning Weather Forecasting

Improving Orbit Prediction Accuracy through Supervised Machine Learning

no code implementations • 15 Jan 2018 • Hao Peng, Xiaoli Bai

Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already.

BIG-bench Machine Learning Collision Avoidance