For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator's input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided, along with an example force protection scenario.

Helmet mounted displays are being considered for a variety of uses in military aviation including training, mission rehearsal, and as a primary flight display in the cockpit. There is a need for considerable research into issues related to the use of this technology and the effects on pilot performance, particularly with respect to spatial orientation. One critical issue is the field-of-view (FOV) necessary when flying military aircraft. The purpose of this research was to investigate the effects of FOV on pilot head movement. Head tilt, pitch, and yaw data were collected in a dome simulator during a low level VMC flight task under three FOV conditions (40,60, and 100 degrees). Results indicated that pilots exhibited reflexive head tilt movements (Opt0 Kinetic Collie Reflex) regardless of FOV. Head pitch and yaw varied as a function of FOV. Pilots made significantly more downward pitch movements with the 40-degree FOV over a wider range of aircraft bank. Head yaw was more prevalent as FOV decreased.BACKGROUND

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