PathfinderX: Resilient GPS Prediction through Machine Learning is a cutting-edge aircraft GPS prediction system, trained on historical data of over 50000+ rows of data that leverages machine learning to revolutionize aviation navigation. In scenarios of GPS outages, this innovative solution predicts aircraft coordinates with remarkable precision:
- Accuracy: ~95% prediction reliability
- Altitude Precision: ±500 feet
- Latitude/Longitude Precision: ±7 kilometers
Designed to enhance aviation safety, PathFinderX offers a scalable and cost-effective navigation solution that requires minimal infrastructure changes.
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Advanced Machine Learning Models
- Random Forest
- LightGBM
- XGBoost
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High Precision Predictions
- Accurate location estimation during GPS disruptions
- Robust predictive algorithms
- Python 3.X.X
- Machine Learning Libraries
- scikit-learn
- pandas
- numpy
- Computational Environment (Jupyter/Google Colab recommended)
# Clone the repository
git clone https://github.com/tejas2510/PathFinderX.git
# Install required dependencies
pip install -r requirements.txt
# To create a dataset depending on your parameters
# Run the dataset script after making changes
python dataset.py
# Now you can run all cells in PathFinderX.ipynbdataset.py- Prepare your flight data, Set parameters like Departure, Arrival Airports, No of flights for training data etc.PathFinderX.ipynb- Configure model parameters, Outage Duration, Animation Duration, Model Hyperparameters.- Run prediction script
- Analyze results
- Implement ensemble models for overall improvement
- Implement Dead Reckoning to predict with a higher accuracy
- Deep learning implementation for a deeper understanding of the historical data relating to weather patterns and other latent features
Contributions are always welcome! Please check our Contributing Guidelines
For more information, collaborations, or inquiries:
- Email: [email protected]
- Discord: #klayjensen
- Project Link: GitHub - PathFinderX
Made with 💖 and Python!