Welcome to the On-Orbit Collision Predictor! 🚀

This project is a passion project I built to help the Canadian Space Agency. It provides a predictive system for on-orbit satellite collision risks. The tool helps assess potential orbital collisions using a blend of machine learning and advanced statistical models. Users can input satellite data, calculate collision probabilities, and manage prediction reports. It is designed for space agencies, satellite operators, and researchers to improve decision-making and avoid costly or dangerous on-orbit collisions.

• Registration & Login: Users can create secure accounts to access the system.
• Profile Management: Users can update profile details and manage their account.

• Data Input: Upload satellite information for collision risk assessments.
• Prediction Results: Generate collision predictions based on machine learning models.
• Reports: Save and manage prediction reports for further analysis.

• User Management: Admins can manage user accounts, including role assignments.
• System Monitoring: Admins can monitor prediction usage and system performance.

• Backend: Django
• Machine Learning: MATLAB for initial calculations, Python (scikit-learn) for machine learning model development
• Frontend: Next.js with D3 for 3D visualization
• Database: PostgreSQL hosted on Supabase

On-Orbit-Collision-Predictor/
│
├── on-orbit-frontend/             # Next.js frontend
│
├── Orbit_Predictor-BackEnd/       # Django backend
│   ├── api/                       # Django app with models, views, serializers, and URLs
│   └── orbit_predictor/           # Main project configuration files
│
├── env/                           # Python virtual environment
│
└── README.md                      # Project README

• Python 3.10+ (but less than 3.13) and Django for the backend to run MATLAB
• Node.js and npm for the Next.js frontend
• MATLAB for initial prediction calculations (optional for extended functionality)
• PostgreSQL for database management
• Supabase for hosted database setup

1. Clone the Repository

   git clone https://github.com/SergioSediq/On-Orbit-Collision-Predictor.git
   cd on-orbit-collision-predictor

2. Install Dependencies

   • Backend: Set up and activate the virtual environment, then install Django and other requirements.

     python3 -m venv env
     source env/bin/activate
     pip install -r requirements.txt

   • Frontend: Navigate to the on-orbit-frontend folder and install dependencies.

     cd on-orbit-frontend
     npm install

3. Database Setup (in progress)

   We're using Supabase for this. Configure the database settings in your .env file. Reference the .env.example file if needed.

4. Inputting CDMs

   In order to input CDMs into the DB, you can use a configured endpoint and send in the CDM data as a JSON object. Here's how:

   Assuming your backend is running on port 8000: Send a request to http://localhost:8000/api/cdms/create/ with your CDM json object. Example:

   { "CCSDS_CDM_VERS": "{{version}}", "CREATION_DATE": "{{creation_date}}", "ORIGINATOR": "{{originator}}", "MESSAGE_ID": "{{message_id}}", "TCA": "{{time_of_closest_approach}}", "MISS_DISTANCE": "{{miss_distance}}", "COLLISION_PROBABILITY": "{{collision_probability}}", "SAT1_OBJECT": "{{sat1_object}}", "SAT1_OBJECT_DESIGNATOR": "{{sat1_designator}}", "SAT1_CATALOG_NAME": "{{sat1_catalog_name}}", "SAT1_OBJECT_NAME": "{{sat1_object_name}}", "SAT1_INTERNATIONAL_DESIGNATOR": "{{sat1_intl_designator}}", "SAT1_OBJECT_TYPE": "{{sat1_object_type}}", "SAT1_OPERATOR_ORGANIZATION": "{{sat1_operator_org}}", "SAT1_COVARIANCE_METHOD": "{{sat1_covariance_method}}", "SAT1_MANEUVERABLE": "{{sat1_maneuverable}}", "SAT1_REFERENCE_FRAME": "{{sat1_reference_frame}}", "SAT1_X": "{{sat1_x}}", "SAT1_Y": "{{sat1_y}}" // continue on with rest of fields }

5. Run DB Migrations

   If you modify the schema:

   cd Orbit_Predictor-BackEnd
   python manage.py makemigrations
   python manage.py migrate

To run both the Django backend and the Next.js frontend concurrently:

npm run dev

This command will start:

• Next.js frontend at http://localhost:3000
• Django backend at http://localhost:8000