This dissertation develops the visual pursuit method for air-to-air tracking and rendezvous of unmanned aircraft systems. It also shows the development of vector-field and proportional-integral methods for controlling UAS flight in formation with other aircraft. The visual pursuit method is a nonlinear guidance method that uses vision-based line of sight angles as inputs to the algorithm that produces pitch rate, bank angle and airspeed commands for the autopilot to use in aircraft control. The method is shown to be convergent about the center of the camera image frame and to be stable in the sense of Lyapunov. In the lateral direction, the guidance method is optimized to balance the pursuit heading with respect to the prevailing wind and the location of the target on the image plane to improve tracking performance in high winds and reduce bank angle effort. In both simulation and flight experimentation, visual pursuit is shown to be effective in providing flight guidance in strong winds. Visual pursuit is also shown to be effective in guiding the seeker while performing aerial docking with a towed aerial drogue. Flight trials demonstrated the ability to guide to within a few meters of the drogue. Further research developed a method to improve docking performance by artificially increasing the length of the line of sight vector at close range to the target to prevent flight control saturation. This improvement to visual pursuit was shown to be an effective method for providing guidance during aerial docking simulations. An analysis of the visual pursuit method is provided using the method of adjoints to evaluate the effects of airspeed, closing velocity, system time constant, sensor delay and target motion on docking performance. A method for predicting docking accuracy is developed and shown to be useful for predicting docking performance for small and large unmanned aircraft systems.