Scalable Streaming Detection Services to support Unmanned Aircraft System Traffic Management (UTM) Ecosystem

Airspace awareness and management has become an increasingly complex task as the advent of unmanned and autonomous vehicles have greatly increased the diversity and capability of airborne platforms, flown by both ally and enemy. Novel platforms such as Electric Vertical Takeoff and Landing (EVTOL) vehicles can enable new modes of commerce, travel, and mission execution but require equally novel solutions for their integration into the National Air Space (NAS). This new áclass of air vehicles, paired with the popularity of pilot-friendly consumer drones, creates increased low-altitude traffic density that requires careful management to prevent congestion and ensure safe operation. This airspace management requires greater flexibility as these craft may operate on-demand, follow dynamic routes, and need to adjust for potential collision threats. Just as these new platforms offer innovative means of operation that benefit society, they offer equal opportunity for nefarious actors, both sophisticated and unsophisticated, who are intent on causing disruption or harm. The conflict in Ukraine shows clearly that sUAS create an asymmetric yet effective means of attack at scaleá á These rapidly emerging technologies demand capable and flexible solutions to maintain aerial dominance. Airspace awareness solutions that are ineffective over large areas, lack scalability, and which cannot swiftly adapt to detect new targets, will fail. As more parties become active members of the airspace through their own operations, from Federal agencies to local police departments, it is imperative that airspace activity be easily aggregated and shared among all, but only those which require it.á á Hidden Level's Airspace Monitoring Service (AMS) is uniquely positioned to play a critical role in the management challenges of today and opportunities of tomorrow. AMS was purpose-built to provide a low-altitude airspace awareness service focused on passive detection of sUAS that is effective over long ranges with few sensors, is economically scalable, and can adapt rapidly to emerging target sets. Hidden Level proposes to instrument Duke Airfield with a low-power passive radio frequency (RF) sensor network. Unlike competing solutions that rely on demodulation of RF signals to discern target location, HL sensors detect using physical RF signal characteristics and localizes by correlating angle estimates from multiple geographically separated sensors. This technology is more robust to changes in RF link protocols, encryption, and spoofed signals than demodulation-based solutions. Hidden Level proposes to further extend its algorithms to more generic RF signal detection, utilizing its unique 3-D direction finding capability to detect, track, and classify airborne RF emitting objects. This capability will provide non-library-based tracking of targets emitting novel RF signals to further enhance the DAF situational awareness and resilience.á