Inertial Navigation System Drift Reduction Using Scientific Machine Learning

Inertial Navigation Systems (INS) are crucial for accurate navigation in GPS-denied environments, but they suffer from drift errors that accumulate over time. This thesis introduces Scientific Machine Learning (SciML) as an innovative approach to mitigate INS drift by integrating physical models with machine learning algorithms. The proposed SciML architecture leverages neural networks to learn complex error patterns and relationships from simulated IMU data, outperforming conventional techniques like Kalman filtering. Utilizing a simulation-focused approach with the Julia programming language and the HighPerformance Inertial Navigation Development Repository (HIDR) library, the research generates realistic datasets encompassing diverse trajectories, sensor errors, and operational conditions. The SciML methodology incorporates data generation, INS mechanization, error modeling using neural networks, and a filtering framework that integrates the Extended Kalman Filter (EKF) with batch filtering techniques. Experimental results demonstrate the superior performance of the SciML-based INS in reducing position, velocity, and attitude errors compared to a baseline Kalman filter. This pioneering approach of fusing SciML with INS physical models holds promise for revolutionizing drift error mitigation and advancing the field of navigation systems, paving the way for more accurate, reliable, and resilient navigation in GPS-denied environments, with potential applications in aviation, robotics, and autonomous vehicles.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• McManus, Matthew

Advisor dc:contributor.advisor

• Edelman, Alan

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156966

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156966