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Predicting and forecasting the position/trajectory of a satellite orbiting Earth

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adityagandhamal/satellite-position-estimation

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About

  • The increase in the number of RSOs (Resident Space Objects) indirectly increases the risk of collision of LEO Satellites.

  • Here's a snippet depicting the scale of the problem (Credits to LeoLabs):

leolabs-google-chrome-2022-01-10-13-54-53_kLfdO0PD_Trim.mp4
  • The important point to be addressed here is the reliable and accurate orbit tracking of satellites to prevent a catastrophic event like the Kessler Syndrome.

  • This project is an experiment on predicting and forecasting the position of a satellite orbiting earth using Deep Learning (LSTM).

  • The LSTM model is trained on the data recorded over 18days and forecasts the trajectory for the next seven days.

The Satellite Trajectory

Figure.1.2022-01-14.14-16-10_Trim.mp4

Forecasted Trajectory

Figure.1.2022-01-14.15-43-23_Trim.mp4

Libraries Used:

  • Tensorflow 2.x
  • Keras
  • Scikit Learn
  • Python >= 3.7
  • Numpy
  • Statsmodels
  • Matplotlib
  • Pandas
  • Pmdarima

Data

This experiment is performed using data of a single satellite.

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Predicting and forecasting the position/trajectory of a satellite orbiting Earth

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