Detect and Avoid Tracker Augmentation (DAATA)

Detect and avoid (DAA) systems are combinations of sensors, trackers, and avoidance algorithms. The sensors provide surveillance information to the DAA trackers which in turn provide the location and velocity of nearby aircraft to the DAA’s alerting and guidance algorithm.  Advanced DAA tracker capabilities, such as those developed by AFRL, can further estimate the traffic’s horizontal turn rate, but cannot reliably predict upcoming turn-ins, roll-outs or changes in vertical profiles.  This limitation is exacerbated when applying DAA in the terminal area where traffic tends to flow in geographically prescribed patterns. Applying the current Transit DAA tracker for departure or arrival operations is expected to lead to elevated nuisance/late alert rates, flow interruptions and decreased safety margin in the terminal area. Terminal Area traffic tends to follow patterns. As such, Bihrle Applied Research (BAR), proposes the use of machine learning to extract these patterns from collected data to enhance DAA tracker output. The DAA Tracker Augmentation (DAATA) system will take tracker outputs, assess them with advanced AI and machine learning, and provide enhance DAA alerting and guidance to DAA systems and AVOs. DAATA will be sensor, tracker, and DAA system agnostic to ensure applicability for a variety of deployment scenarios.