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.

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

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

Libraries Used:

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.

Name		Name	Last commit message	Last commit date
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Univariate Forecasting		Univariate Forecasting
README.md		README.md
Trajectory Forecasting.ipynb		Trajectory Forecasting.ipynb