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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Gavilanez Gallardo, Gabriela Carolina"],"institution":null,"degree_name":"Master of Science in Aerospace Engineering","degree_level":"Thesis - ERAU Login Required","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-10-01T07:00:00Z","date_published":"2024-10-01T07:00:00Z","updated_at":"2026-07-27T19:26:16Z","subjects":["Machine Learning","Artificial Intelligence","Computer Vision","Navigation","Parameter Estimation","Aerospace Engineering","Navigation, Guidance, Control and Dynamics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/861","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Gavilanez Gallardo, Gabriela Carolina"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - ERAU Login Required"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Artificial Intelligence","Computer Vision","Navigation","Parameter Estimation","Aerospace Engineering","Navigation, Guidance, Control and Dynamics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/861"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Machine Learning-Aided Aerospace Applications with Generative Adversarial Networks"]}]}],"canonical_facts":{"dc:creator":["Gavilanez Gallardo, Gabriela Carolina"],"dc:description.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.
"],"dc:identifier":["https://commons.erau.edu/edt/861"],"dc:subject":["Machine Learning","Artificial Intelligence","Computer Vision","Navigation","Parameter Estimation","Aerospace Engineering","Navigation, Guidance, Control and Dynamics"],"dc:title":["Machine Learning-Aided Aerospace Applications with Generative Adversarial Networks"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - ERAU Login Required"],"thesis:degree_name":["Master of Science in Aerospace Engineering"]},"updated_at":"2026-07-27T19:26:16Z"}