PNNL

Abstract

Effective utilization of all available intelligence, including sensor signatures and situational awareness is a key objective in homeland security. Binding all sources of information into an objective and lucid decision algorithm can provide clarity to identify signatures that are strongly and uniquely indicative of terrorist activities, thus reducing false alarms that conjure images of profiling and concerns regarding our civil rights. The fundamental premise of this paper is that the optimal integration of situational awareness, intelligence and hard sensor signatures should begin at the field level and work backward, that is, begin with the desired outcome and work backward. Construction of in-the-field algorithms with these characteristics will necessarily be dominated by careful mathematical and scientific thought as opposed to purely empirical, unguided data analysis. The research and development (R&D) effort for optimal decision algorithm construction naturally encourages homeland security communication at all operational levels including that between scientists, intelligence analysts, government leadership and the private sector. Why? Because decisions have consequences that impact all stakeholders, and a formal decision framework is capable of quantifying these consequences. A properly constructed framework naturally includes mathematical plug-in points for hard sensor data, intelligence and situational awareness. These plug-in points naturally guide the formulation of information to a common standard, thus facilitating and promoting intelligence sharing. A well established foundation to build these frameworks at the in-the-field and strategic level can be found in a body of theory in mathematical statistics -Bayesian decision sciences. We assert that decision algorithms with these characteristics are necessary for optimal front line operational capabilities in the war on terrorism.