Machine Learning-Aided Aerospace Applications with Generative Adversarial Networks

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.