🚀 Space Traffic Management (STM) Simulation

A Python-based Space Traffic Management (STM) Simulation project that analyzes satellite and debris trajectories using TLE datasets, detects close approaches, estimates collision risk, and produces CSV outputs for visualization and ML-based extensions.

📌 Project Overview

With the rapid increase in satellites and orbital debris, managing space traffic has become critical. This project simulates orbital objects, detects close approaches, and estimates collision risks using orbital mechanics and statistical analysis.

🎯 Objectives

• Load and validate TLE (Two-Line Element) datasets.
• Propagate satellite and debris orbits.
• Detect close approaches between objects.
• Compute collision risk probabilities.
• Generate CSV files for analysis and visualization.
• Enable ML-based risk prediction extensions.

🧠 Key Features

• Supports real-world and dummy TLE datasets.
• Handles active satellites, debris, and space stations.
• Modular and scalable architecture.
• CSV-based outputs for easy visualization.
• Ready for ML and 3D visualization integration.

📥 Dataset Format

Each object must be defined using valid TLE format:

OBJECT NAME
1 XXXXXU XXXXXA   XXXXX.XXXXXXXX  .XXXXXXXX  XXXXX-X  XXXXX-X 0 XXXX
2 XXXXX XX.XXXX XX.XXXX XXXXXXX XXX.XXXX XX.XXXX XXXXXXX

Ensure the dataset has multiples of three lines.

⚙️ Installation

git clone https://github.com/your-username/STM_TLE.git
cd STM
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

▶️ Execution

python src/main.py

📊 Output Files

📄 File	📘 Description
close_approach.csv	Detected close encounters
collision_risk.csv	Collision probability estimates

📈 Visualization

python src/visualize_distance.py
python src/visualize_risk_levels.py
python src/visualize_station_safety.py
python src/visualization_3d.py

🧪 Results

This section summarizes the simulation outcomes, comparing conditions before and after applying Space Traffic Management (STM) strategies.

🔹 Station–Debris Safety Improvement

• Shows the minimum distance between space stations and debris objects across multiple events.

• Post-STM distances consistently increase.

• Indicates improved orbital safety.

🔹 Risk Level Comparison (Active vs Debris)

• Compares collision risk levels before and after STM implementation.

• Reduction in high-risk encounters

• Shift toward medium and low-risk events

🔹 Active Satellites vs Debris — Distance Distribution

• Displays the distribution of close-approach distances.

• Fewer low-distance encounters post-STM

• Higher separation distances dominate

🔹 Three-Dimensional Proximity-Based Collision Risk Visualization for Active Satellites and Space Debris

• 3D Risk Visualization: Displays detected satellite–debris conjunction events in a three-dimensional risk space for intuitive analysis.

• Distance-Based Severity: Color intensity represents separation distance, highlighting high-risk close-approach events.

• Risk Pattern Insight: Clusters of points indicate regions with elevated collision probability, demonstrating STM effectiveness.

📌 Overall Observation

The results demonstrate that STM-based monitoring and control mechanisms significantly improve orbital safety by increasing separation distances and reducing collision risks.

🤖 Future Enhancements

• Machine Learning-based collision prediction.
• Real-time TLE updates.
• Interactive 3D visualization.
• Automated avoidance maneuver suggestions.

🧪 Testing Notes

• Validate TLE files before execution.
• Start with dummy datasets.
• Check logs if CSV files are empty.

👥 Team Contribution Split

• Aditya Srivastava(Github Id - aadi02anu07) - TLE parsing - Orbit propagation - Proximity detection

• Ishani Bhushan(Github Id - Ishani1204) - Risk assessment - Visualization - ML integration

📄 License

This project is intended for academic and research purposes.

🚀 Space is getting crowded, manage it responsibly.