Hierarchical Software Framework for Safe Unmanned Aerial Systems Integration into National Airspace (NAS)

Unmanned aerial systems (UAS) have diverse applications and rely on GPS technology for providing information on aircraft location and position. However, a number of factors, including air conditions, satellite configuration, and interference from other sources, can impact GPS signals. GPS is also vulnerable to cyber security attacks and threats, including jamming, spoofing, and false data injection. The threats to GPS are depend on various factors and the integrity of timestamps could be compromised by cyber-attacks. This paper provides a conceptual software framework that integrates machine learning workflow, zero-trust and reinforcement learning to mitigate interference-free environment for UAS navigation. The framework is structured as a hierarchical design: sensing, communication, and control layers to host cyber physical and computing elements.