Date of Award


Document Type

Dissertation - Open Access

Degree Name

Doctor of Philosophy in Aviation


College of Aviation

Committee Chair

Dothang Truong, Ph.D.

First Committee Member

Haydee M. Cuevas, Ph.D.

Second Committee Member

Bruce A. Conway, Ph.D.

Third Committee Member

Michael K. Zuschlag, Ph.D.


The purpose of this study was to determine the key physical variables for visual detection of small, Unmanned Aircraft Systems (UAS), and to learn how these variables influence the ability of human pilots, in manned-aircraft operating between 60-knots to 160-knots in the airport terminal area, to see these small, unmanned aircraft in time to avoid a collision. The study also produced a set of probability curves for various operating scenarios, depicting the likelihood of visually detecting a small, unmanned aircraft in time to avoid colliding with it. The study used the known limits of human visual acuity, based on the mechanics of the human eye and previous research on human visual detection of distant objects, to define the human performance constraints for the visual search task.

The results of the analysis suggest the probability of detection, in all cases modeled during the study, is far less than 50 percent. The probability of detection was well under 10 percent for small UAS aircraft similar to the products used by many recreational and hobby operators.

The results of this study indicate the concept of see-and-avoid is not a reliable technique for collision prevention by manned-aircraft pilots when it comes to operating near small, unmanned aircraft. Since small, unmanned aircraft continue to appear in

airspace where they do not belong, regulators and the industry need to accelerate the development and deployment of alternative methods for collision prevention between sUAS aircraft operations and manned-aircraft.

The analysis effort for this study included the development of a new simulation model, building on existing models related to human visual detection of distant objects. This study extended existing research and used currently accepted standards to create a new model specifically tailored to small, unmanned aircraft detection. Since several input variables are not controllable, this study used a Monte Carlo simulation to provide a means for addressing the effects of uncertainty in the uncontrollable inputs that the previous models did not handle. The uncontrollable inputs include the airspeed and direction of flight for the unmanned aircraft, as well as the changing contrast between the unmanned aircraft target and its background as both the target aircraft and the observer encounter different background and lighting conditions.

The reusable model created for this study will enable future research related to the visual detection of small, unmanned aircraft. It provides a new tool for studying the difficult task of visually detecting airborne, small, unmanned aircraft targets in time to maneuver clear of a possible collision with them. The study also tested alternative input values to the simulation model to explore how changes to small, unmanned aircraft features might improve the visual detectability of the unmanned aircraft by human pilots in manned aircraft. While these changes resulted in higher probabilities of detection, the overall detection probability remained very low thereby confirming the urgent need to build reliable collision avoidance capability into small UAS aircraft.