Enhancing entity level knowledge representation and environmental sensing in COMBATXXI using unmanned aircraft systems

Abstract

Current modeling and simulation techniques may not adequately represent military operations using unmanned aircraft systems (UAS). A method to represent these conditions in a combat model can offer insight to the use and application of UAS operations, as well as understanding the sensitivity of simulation outcomes to the variability of UAS performance. Additionally, using combat model simulations that do not represent UAS behavior and conditions that cause this variability may return misleading or incomplete results. Current approaches include explicit scripting of behaviors and events. We develop a proof of principle search, targeting, and acquisition (STA) model for use with UAS within COMBATXXI, leveraging existing STA research conducted at the MOVES Institute at the Naval Postgraduate School. These dynamic behaviors are driven by events as they unfold during the simulation run rather than relying on preplanned events as in the scripted approach. This allows these behaviors to be highly reusable since they do not contain scenario or incident specific information. We demonstrate the application of the new STA model in a tactical convoy scenario in COMBATXXI. A design of experiments and post analysis quantifies the sensitivity of the measures of effectiveness of success to conditions contributing to variability in UAS performance.