Security of U.S. military bases is of high interest and operational importance to the U.S. military and allied forces. The Situational Awareness for Surveillance and Interdiction Operations (SASIO) model was developed and designed to simulate the operational tasking of a single Unmanned Aerial Vehicle (UAV) and a ground-based interceptor that are designed to search, identify, and intercept potential hostile targets prior to reaching a military base. This research uses design and analysis of experiments to study and explore insights for the tactical employment of a UAV and an interceptor to combat potential hostile actions against a predefined area of interest. Optimal design of experiments and generalized linear models are used to create surrogate models that quantify the success rates of interception based on both employment strategies for the UAV and ground-based interceptor and also characteristics of the mission. The results provide guidance for tactical employment of Blue Force assets, as well as provide insights to influence Red Force behavior in an advantageous manner.
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