Sensor Resource Management to Support UAS Integration into the National Airspace System

摘要

The National Air Space (NAS) is often described as a complex aviation system-of-systems that seamlessly works in harmony to provide safe transit for all aircraft within its domain. The number of aircraft within the NAS is growing and according the FAA, "[o]n any given day, more than 85,000 flights are in the skies in the United States...this translates into roughly 5,000 planes in the skies above the United States at any given moment. More than 15,000 federal air traffic controllers in airport traffic control towers, terminal radar approach control facilities and air route traffic control centers guide pilots through the system". The expected increase in projected density of aircraft, including integration of UAVs, requires innovative resource allocation methodologies to task and re-task sensors (radar systems) to effectively monitor airborne vehicles and ensure aircraft adhere to the minimum separation requirement. Our approach is to use an evolutionary algorithm to evolve the task assignment. In addition, we utilize the Kalman Filter's covariance matrices to determine positional uncertainty in our estimates to predict if a separation requirement is violated.