Data Science to Analyze TLE Data and Orbital Predictions

Two-line Element (TLE) data is used to determine the position of an Object currently orbiting the earth, such as satellites, at any given time. The understanding of these movements plays a big role in navigation, communication, and enhancing aerospace research. However, natural factors such as space weather impact the accuracy of these movements, yet often not fully considered. This project analyzes these movements and visualizes them, using tools for parsing, analyzing, processing, and predicting the data. Over the course of the project, Skyfield, NumPy, and Pandas were utilized to handle processing and computations. Space weather data was then integrated using APIs in order to factor them into the predictive aspects and various visualization tools were used to create graphs that can be easily interpreted. A web-based platform was developed, allowing users to visualize any TLE data interactively, contributing to improved predictive accuracy and a deeper understanding of orbital dynamics and the influence of space weather.