Artificial Intelligence-Assisted Inertial Geomagnetic Passive Navigation

<p>In recent years, the integration of machine learning techniques into navigation systems has garnered significant interest due to their potential to improve estimation accuracy and system robustness. This doctoral dissertation investigates the use of Deep Learning combined with a Rao-Blackwellized Particle Filter for enhancing geomagnetic navigation in airborne simulated missions.</p> <p>A simulation framework is developed to facilitate the evaluation of the proposed navigation system. This framework includes a detailed aircraft model, a mathematical representation of the Earth's magnetic field, and the incorporation of real-world magnetic field data obtained from online databases. The setup allows an accurate assessment of the performance and effectiveness of the proposed Geomagentic architecture in diverse and realistic geomagnetic scenarios.</p> <p>The results of this research demonstrate the potential of Machine Learning algorithms in improving the performance of the sensor fusion filter for geomagnetic navigation, and introduces a novel approach for resolution enhancing of available geomagnetic models, which provides a better description of the magnetic features within these models. The integration leads to more accurate and robust inertial guidance in airborne missions, thus paving the way for advanced, reliable navigation systems for a variety of aerial vehicles.</p> <p>Overall, this dissertation contributes to the state-of-the-art in geomagnetic navigation research by offering a novel approach to integrating machine learning techniques with traditional estimation methods, with a novel technique to obtain more accurate geomagnetic models required within these navigation architectures. The findings of this work hold promise for the development of advanced, adaptive navigation systems for both civilian and military aviation applications.</p>

Name thesis:degree_name

Doctor of Philosophy in Aerospace Engineering

Level thesis:degree_level

Dissertation - Open Access

Discipline thesis:degree_discipline

Aerospace Engineering

Year

2023

Author dc:creator

• Cuenca, Andrei

• Geomagnetic Navigation
• Generative Adversarial Networks
• Machine Learning
• Airborne Navigation
• Particle Filtering
• Dead reckoning
• Navigation, Guidance, Control and Dynamics

Repository record dc:identifier

https://commons.erau.edu/edt/773

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1801