Airborne Visual Detection of Small Unmanned Aircraft Systems with ADS-B
Jamey D. Jacob, School of Aerospace Engineering, Oklahoma State University Jon Loffi, School of Aviation, Oklahoma State University Taylor Mitchell, Unmanned Systems Research Institute, Oklahoma State University Matt Vance, School of Aviation, Oklahoma State University Ryan Wallace, Professor of Aerospace Science, Polk State College
One key challenge of integrating Unmanned Aircraft Systems (UAS) platforms into the National Airspace System is the potential for midair collisions between manned aircraft and the unmanned system. The lack of an established UAS benchmark for Detect, Sense and Avoid Systems put the preponderance of avoidance efforts on manned aircraft pilots to visually see and avoid potential collision threats. The small size, unusual configurations, and diverse operational applications of unmanned systems make UAS platforms difficult to visually identify. This paper sought to determine the mean visibility distance of small UAS systems (sUAS) to an alerted pilot flying a general aviation aircraft in visual meteorological conditions (VMC). The study evaluated mean visibility distance to various small UAS platforms based on a scripted set of UAS convergence conditions. The study utilized a quantitative, experimental design in which a general aviation aircraft was flown into a UAS operations area. Study pilots were instructed to locate a flying UAS aircraft without bearing assistance. Both the UAS and manned aircraft were assigned vertically de-conflicted altitudes with the UAS aircraft executing a series of converging and crossing courses relative to the manned aircraft. The distance at which the pilot visually located the UAS platform was timestamped and electronically recorded via a GPS tracking device. The various conditions were statistically analyzed to determine significant visibility differences among the various convergence conditions.