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

Here's the link to the dataset https://www.kaggle.com/idawoodjee/predict-the-positions-and-speeds-of-600-satellites
The dataset from Kaggle contains information of 600 satellites.

This experiment is performed using data of a single satellite.