Detection of aircraft below the horizon for vision-based detect and avoid in unmanned aircraft systems

Abstract

Vision-based aircraft detection technology may provide a credible sensing option for automated detect and avoid in small-to-medium size fixed-wing unmanned aircraft systems (UAS). Reliable vision-based aircraft detection has previously been demonstrated in sky-region sensing environments. This paper describes a novel vision-based system for detecting aircraft below the horizon in the presence of ground clutter. We examine the performance of our system on a data set of 63 near collision encounters we collected between a camera-equipped manned aircraft and a below-horizon target. In these 63 encounters, our system successfully detects all aircraft, at an average detection range of 1890 m (with a standard error of 43 m and no false alarms in 1.1 h). Furthermore, our system does not require access to inertial sensor data (which significantly reduces system cost) and operates at over 12 frames per second.