Inertial Navigation System for Aircraft and Spacecraft

This research investigates the integration of Machine Learning (ML) algorithms into Inertial Navigation Systems (INS) to address the growing limitations faced by traditional INS in increasingly complex aerospace missions. While reliable, traditional INS suffer from accumulating errors over time due to sensor noise and integration drift. This project proposes incorporating sophisticated algorithms like Genetic Algorithms and Artificial Neural Networks to dynamically correct these errors, re-calibrate sensors, and compensate for drift in real-time. This innovative approach has the potential to revolutionize aerospace navigation by providing highly accurate and reliable systems, paving the way for advancements in exploration and pushing the boundaries of human ingenuity in space.