Currently, in the field of military modernization, tactical networks using advanced unmanned aerial vehicle systems, such as drones, place an emphasis on proactively preventing operational limiting factors produced by cyber-electronic warfare threats and responding to them. This characteristic has recently been highlighted as a key concern in the functioning of modern network-based combat systems in research on combat effect analysis. In this paper, a novel discrete-event-system-specification-based cyber-electronic warfare M&S (D-CEWS) was first proposed as an integrated framework for analyzing communication effects and engagement effects on cyber-electronic warfare threats and related countermeasures that may occur within drones. Accordingly, for the first time, based on communication metrics in tactical ad hoc networks, an analysis was conducted on the engagement effect of blue forces by major wireless threats, such as multi-layered jamming, routing attacks, and network worms. In addition, the correlations and response logics between competitive agents were also analyzed in order to recognize the efficiency of mutual engagements between them based on the communication system incapacitation scenarios for diverse wireless threats. As a result, the damage effect by the cyber-electronic warfare threat, which could not be considered in the existing military M&S, could be calculated according to the PDR (packet delivery ratio) and related malicious pool rate change in the combat area, and the relevance with various threats by a quantifiable mission attribute given to swarming drones could also be additionally secured.

The importance of the weapon target assignment problem is increasing as weapon systems are expected to become intelligent and unmanned on the future battlefield. This study discusses the weapon-target assignment in small-scale ground force combat, which has not been addressed much in other studies. In ground combat, target assignment means the start of an engagement, so assignment should be made taking into account not only the probability of killing an enemy but also the probability of surviving friendly forces. We introduce the WTAG (WTA ground) model that reflects these characteristics and describes methodologies for solving the problem including mixed integer nonlinear problems and Lagrangian relaxation approaches. Our finding is that the algorithm presented can efficiently provide the bounds of the problem in a practical size.

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Wargame is an important tool that enables training units to develop various strategies by allowing them to experience unexpected situations. There are three methodologies that determine the behavior of the Computer Generated Forces(CGF) in wargame-rule-based, agent-based, and learningbased methodologies. The military determines the behaviors of the CGF mainly based on the rules because a doctrine and an operation plan are well established. However, the advent of intelligent weapons and the accompanying changes in tactics will make it difficult to expect an environment and situations of the future battlefield. Therefore, we studied the automation of CGF through reinforcement learning in order to give unexpected situations, so that the training unit would be able to establish various strategies and tactics through the wargame model. Based on the combat functions of the ground forces, we configured multiple environments that the ground forces CGFs will learn in. First, infantry and artillery CGFs learned in the close combat environment, which is the basis of ground forces combat. Second, the trainee CGF learned in the context of military training. Third, the drone CGF learned how to reconnaissance and attack in a multidrone environment, and finally, the combat service support CGF learned under the mission of supplying ammunition. As a result, we confirmed that the reinforcement learning methodology is applicable to CGF through these experiments.