Date of Award
Fall 2024
Access Type
Thesis - ERAU Login Required
Degree Name
Master of Science in Aerospace Engineering
Department
Aerospace Engineering
Committee Chair
Hever Moncayo
First Committee Member
Richard Prazenica
Second Committee Member
Morad Nazari
College Dean
James W. Gregory
Abstract
Applied generative machine-learning models have demonstrated exceptional accuracy at recreating realistic data, becoming a highly researched field in aerospace and defense technologies. Generative Adversarial Networks (GANs), a subset of generative models, have shown remarkable proficiency at this task, surpassing other state-of-the-art approaches. They have become an extraordinary tool for addressing critical challenges like data scarcity in complex environmental conditions. This work focuses on exploiting the capabilities of GANs for aerospace applications, including point cloud-based attitude and position estimation of non-cooperative targets, geomagnetic navigation, and pilot behavior estimation. Through a detailed study of these applications, this research aims to showcase the versatility and efficiency of GANs in addressing specific aerospace needs. By leveraging the advanced capabilities of GANs, this study enhances the accuracy and reliability of aerospace systems and opens new avenues for innovation in the field.
Scholarly Commons Citation
Gavilanez Gallardo, Gabriela Carolina, "Machine Learning-Aided Aerospace Applications with Generative Adversarial Networks" (2024). Doctoral Dissertations and Master's Theses. 861.
https://commons.erau.edu/edt/861
