Satellite Tracking and Trajectory Prediction

Satellite Tracking and Trajectory Prediction Application

This project enhances Space Situational Awareness (SSA) by providing real-time satellite tracking and trajectory prediction for Earth’s orbit.

The application integrates open-source Two-Line Element (TLE) data with both traditional mathematical models and an experimental Long Short-Term Memory (LSTM) machine learning model for precise satellite trajectory prediction. Additionally, it includes a rocket launch simulation tool to predict trajectories based on input parameters.

Key Features:

Real-time satellite tracking across all Earth orbits.

Prediction of satellite trajectories using traditional models and LSTM machine learning.

Rocket launch simulation based on input parameters like thrust and weather conditions.

3D satellite visualization using Three.js.

Integration of TLE data for satellite tracking.

Technologies Used:

Frontend: React.js, Three.js, React-Three-Fiber, React-Bootstrap.

Backend: Python Flask, TensorFlow (LSTM Model), Satellite.js for TLE data.

APIs: CelesTrak (satellite data), OpenWeatherMap (weather data for launch simulation).

Parikshit Padole