Inertial Navigation System for Aircraft and Spacecraft

Author(s)	Arvind Suthar, Nishant Gawade, Yash Kava, Nathen Tuscano, Nitika Rai
Country	India
Abstract	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.
Keywords	Machine Learning, Inertial Navigation System, Algorithm, Genetic Algorithm
Field	Computer
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-10
Cite This	Inertial Navigation System for Aircraft and Spacecraft - Arvind Suthar, Nishant Gawade, Yash Kava, Nathen Tuscano, Nitika Rai - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.32836
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.32836
Short DOI	https://doi.org/g8vgf8

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