Intelligence, surveillance, and reconnaissance unmanned aircraft systems (UAS) are the most common variety of UAS in use today and provide invaluable capabilities to both the military and civil services. Keeping the sensors centered on a point of interest for an extended period of time is a demanding task requiring the full attention and cooperation of the UAS pilot and sensor operator. There is great interest in developing technologies which allow an operator to designate a target and allow the aircraft to automatically maneuver and track the designated target without operator intervention. Presently, the barriers to entry for developing these technologies are high: expertise in aircraft dynamics and control as well as in real- time motion video analysis is required and the cost of the systems required to flight test these technologies is prohibitive. However, if the research intent is purely to develop a vehicle maneuvering controller then it is possible to obviate the video analysis problem entirely. This research presents a solution to the target tracking problem which reliably provides automatic target detection and tracking with low expense and computational overhead by making use of the infrared sensor from a Nintendo Wii Remote Controller.

Research in Unmanned Air Vehicles (UAV's) continues to push the limitations of size and weight. As technical advances have made UAV's smaller and less expensive, they have become more flexible and extensive in their roles. To continue using smaller and less expensive components while retaining and even enhancing performance requires more sophisticated processing of sensor data in order for the UAV to accurately determine its state and thereby allow the use of feedback in controlling the aircraft automatically. This work presents a three-stage state-estimation scheme for the class of UAV's know as Miniature Air Vehicles (MAV's). The first stage estimates pitch and roll, the second stage estimates heading, and the third stage produces a position estimate and an estimate of wind speed and direction. All three stages make use of the extended Kalman filter, a framework for using a system dynamic model to predict future states and to update the predictions using weighted sensor measurements as they become available, where the weighting is based on the relative uncertainty of the dynamic model and the sensors. Using the three-stage state esti-mation scheme, significant improvements in the estimation of pitch, roll and heading have been achieved in simulation and flight testing. Performance of the navigation (position and wind) stage is comparable to an existing baseline algorithms for position and wind, and shows additional promise for use in dead reckoning when GPS updates become unavailable.