UTILIZING A FACTION DISPOSITION PARTICLE FILTER (FDPF) FOR TRACKING OPPOSING FORCES (OPFOR) ENTITY POSITIONS IN LAND-BASED MILITARY SIMULATIONS WITH PARTIAL OBSERVABILITY

Abstract

This thesis investigates how a Faction Disposition Particle Filter can be effectively designed to estimate the positions of opposing force (OPFOR) entities under conditions of fog of war on land-based military simulations with partial observability. Inspired by military planning procedures, the proposed method models entire faction-level enemy courses of action (COAs) as individual particles. Unlike conventional approaches that estimate unit positions independently, each particle represents the coordinated behavior of all OPFOR units, capturing their collective intent and operational coherence. This enables the decision-making AI to reason over complete enemy dispositions rather than fragmented unit data. The implemented prototype consists of three core components: a set of Particle AIs encoding plausible OPFOR COAs, a Likelihood Estimator assessing their consistency with current observations, and a decision-making AI that selects actions based on the weighted particle states. Evaluated across a range of scenarios, the approach demonstrated improved estimation and adaptive planning under uncertainty. The modular design, observed limitations, and identified trade-offs offer a foundation for future enhancements, including dynamic particle management, improved likelihood modeling, and predictive planning.Distribution Statement A. Approved for public release: Distribution is unlimited.Major, German Arm