Abstract

The evolution of the modern battlefield is increasingly complex as new technologies emerge. However, the nature of the battlefield still can be explained by Baron De Jomini’s “Grand Tactics.” Military planners’ success is in their ability to develop synergy through layering effects of a complex system at a decisive point on the battlefield. Synergistic effects require subject matter experts (SME) working in planning cells to integrate systems and units in time and space. These planning cells have large footprints and become prioritized in the opposition’s targeting cycle. Without these SMEs, planners are unable to identify the latent variables to achieve the massing of forces at the decisive point. This paper explores the application of Hidden Markov Models (HMMs) to enhance existing Correlation of Forces and Means (COFM) calculators as predictive tools in military planning. Current tools focus on a 3 to 1 force ratio for an offensive operation. They get improved through applying a force equivalent factor. The force equivalent factors are scalars that adjust relative combat power from a single main battle tank to a mechanized infantry vehicle. These tools lack the ability to identify the physics of the battlefield in time and space. The utilization of wargames during planning and training provides a venue for serious games to improve planning tools. It allows a visualization of target pairing and dynamics that a linear equation would miss. This study employs scenarios generated in OneSAF, ranging from simple platoon-level ambushes to combined arms maneuver featuring rotary wing assets requiring a shaping effort to ensure favorable COFMs. The focus of the research lies in leveraging HMMs to establish a time-series indicator of success probability using potentially observable data, with an emphasis on communication dynamics. In this study, we examine observable communication by data generated through both visual and direct contact. Through observation of contact from the friendly and opposition forces will provide a predictor of the hidden state of relative advantage, in time and space. With the data generated by the OneSAF simulation, the HMM determines the states of the operation and probability of success while minimizing required presence inside the units.

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