Satellite Targeting

Student: Surin Denis

Supervisor: Andrey Ustyuzhanin

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2021

Satellite targeting is important problem for modern astrophysics. All existing models are based on physical laws and because of lack or inaccuracy of information, their predictions are inaccurate. This leads to collisions of satellites, which in addition to momentary losses may further lead to the impossibility of using the orbit due to more space garbage. Because of these reasons, creating a more accurate model is important problem which has not yet been solved. In the paper I am going to study the literature and apply the methods of classical machine learning for achieving baseline results. In the future, the predictions can be improved by using different types of recurrent neural networks: vanilla, LSTM, GRU in the encoder/decoder or stack of layers architectures.

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