Reducing Uncertainty in Effects-Based Operations
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Known as the fog of war, uncertainty has been prevalent in the conduct of military operations throughout human history. Intelligence collection efforts are tasked to reduce this uncertainty through the collection of information. Utilizing Shannon's entropy as a measure of the expected information gain due to an intelligence collection effort, a methodology is developed to prioritize and allocate intelligence assets in an efficient manner. Incorporated in this methodology are target priority and the requirement to reassess dynamic targets. The application area for the methodology is Effects-Bas...
Known as the fog of war, uncertainty has been prevalent in the conduct of military operations throughout human history. Intelligence collection efforts are tasked to reduce this uncertainty through the collection of information. Utilizing Shannon's entropy as a measure of the expected information gain due to an intelligence collection effort, a methodology is developed to prioritize and allocate intelligence assets in an efficient manner. Incorporated in this methodology are target priority and the requirement to reassess dynamic targets. The application area for the methodology is Effects-Based Operations. A generalized state model is developed to conduct adversary system-of-systems analysis. This model forms the basis for the entropy calculations and the resultant integer program to maximize the information gain.
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