OPTIMIZING INTELLIGENCE, SURVEILLANCE, AND RECONNAISSANCE INPUTS FOR THE SYNTHETIC THEATER OPERATIONS RESEARCH MODEL

We consider the task of a mission planner for a unit charged with conducting intelligence, surveillance, and reconnaissance (ISR) on an area of operations in a theater-level conflict for enemy combatants using the Synthetic Theater Operations Research Model (STORM). This relies upon effective intelligence planner inputs regarding revisit interval and sensor resolution for enemy combatants. Further complicating the problem is that the entire area of operations (AO), encompassing large geographical expanses, must be searched within the mission’s time constraints. Mission planning to coordinate the search assets and cover the search area while meeting intelligence inputs has proven to be difficult, often producing infeasible solutions. We present a mixed-integer linear program that effectively prescribes plans for assets to search optimally in the AO. The program is applicable, implementable, and adaptable to STORM's settings across all campaigns and produces an average of 54.6% and a median of 22.8% improvement in search coverage relative to existing heuristics.Outstanding ThesisMajor, United States Marine CorpsApproved for public release. Distribution is unlimited