Small xed-wing UAS (SUAS) such as Raven and Unicorn have limited power, speed, and maneuverability. Their
missions can be dramatically hindered by environmental conditions (wind, terrain), obstructions (buildings, trees)
blocking clear line of sight to a target, and/or sensor hardware limitations (xed stare, limited gimbal motion,
lack of zoom). Toyon's Sensor Guided Flight (SGF) algorithm was designed to account for SUAS hardware
shortcomings and enable long-term tracking of maneuvering targets by maintaining persistent eyes-on-target.
SGF was successfully tested in simulation with high-delity UAS, sensor, and environment models, but real-
ight testing with 60 Unicorn UAS revealed surprising second order challenges that were not highlighted
by the simulations. This paper describes the SGF algorithm, our rst round simulation results, our second order
ight testing, and subsequent improvements that were made to the algorithm.