Passive Non-Cooperative Intruder State Estimation and Optimal-Feedback Avoidance System for UAVs

In recent years, numerous applications for unmanned aircraft systems (UAS) have emerged, such as manufacturing inspections and reconnaissance. Ensuring safety is crucial for integrating UAS into the National Airspace System (NAS); this integration is being conducted on the basis of a century of experience that has made manned aircraft operations incredibly safe.

A key challenge for unmanned flight is the inability to "detect-and-avoid" (DAA) obstacles. Various DAA systems have been proposed in recent years, each employing different sensor modalities. Cooperative systems enable air vehicles to exchange state information, while devices like the Automatic Dependent Surveillance-Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS) use satellite navigation sensors and transponders, respectively, to broadcast position data. Additionally, the Airborne Collision Avoidance System (ACAS) led to the creation of the ACAS-XU standard for unmanned aircraft.

The DAA capability for UAS must be extended to address non-cooperative intruders. This paper introduces an integrated vision-based passive collision alert system (PCAS) and guidance system that is designed to detect and optimally avoid collision with non-cooperative intruders. The system can adhere to recently-introduced regulations for safety zones and can be customized pre-flight. Hardware-in-the-loop (HITL) simulation demonstrates the feasibility for deployment on UAS in a plug-and-play fashion.