Decision Advantage is a goal in current and future military operations. Achieving such an advantage can be done by degrading adversaries’ decision-making ability through imposition of complexity into the decision problems they have to make. This paper describes mathematical techniques for quantifying decision complexity in Integrated Air Defense Systems (IADS). The methods are based on graph properties derived from the defender’s IADS’ System of Systems description and the attacker’s Course of Action (COA) plans. Multiple plans can be compared quantitatively with respect to the decision complexity they impose on the defender. using metrics that are semantically meaningful to planners. The metrics developed are able to expose subtle ways that COAs impose complexity on an adversary, that may not be obvious to an operational planner at first glance.
Analysis of imagery derived from low-earth orbiting satellites has a long history going back into the midst of Cold War era. For a long time, these imagery have been provided by military satellites and mainly used by the Department of Defense (dod) and Intelligence Community (ic) analysts. Since the mid of 1990s, the international constellation of commercial satellites has been growing with increasing temporal and spatial resolutions such as Maxar constellation currently provides nearly four million square kilometers per day translating into a staggering 100TB of imagery every day. These satellites thus enable tremendous opportunities for various government and commercial tasks. In this paper, we present our software framework combining state-of-the-art object detection and change identification algorithms with statistical learning techniques to detect various objects-of-interest (permanent- and semipermanent-structures and vehicles) and learn their behaviors. Our approach is applicable for detecting both macro- and micro- scale changes by turning vast amount of imagery collected by commercial satellites into information and information into actionable insight.
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