Abstract
Due to the increasing number of entities and more powerful sensors involved in beyond-visual-range air combat missions, the amount of situational information to be considered by aircrews is growing exponentially. To enable pilots to handle and effectively utilize these volumes of information, it is necessary to provide them with appropriate training in simulators. Here, the further development of the simulated opposing forces is of particular importance to confront pilots with constantly new challenges. In order to sufficiently represent enemy forces and their team behavior, the information processing procedures in modern airborne warfare must be transferred towards the computer-generated forces (CGF) behavior models. To meet these demands, conducting a comprehensive situation analysis is essential. Therefore, in this paper, we present a conceptual approach for a situation analysis that is utilized later on to facilitate tactical behavior planning on the adversary side in training simulators or threat evaluations. In our proposed concept, the situation analysis process is partitioned into three sequential segments. It begins with the collection and sorting of situation data, incorporating subtasks such as identifying the enemy’s intent and addressing data uncertainties. As a subsequent step, a risk analysis is described to identify potential risks and to evaluate their potential impact for the fighter team. Subsequently, we employ methods to evaluate the probability of these risks and combine them to calculate an impact analysis. Finally, we explore methods that perform a situation assessment, which serves as the foundation for CGF tactical behavior planning. In the conclusion, we outline first implementations in our simulation frame.
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Ruther, P., Strohal, M., Stütz, P. (2025). A Situation Analysis Process in Computer-Generated Forces Team Behavior Within Air Combat Simulations Under Risk and Uncertainty: Concept and First Implementations. In: Mazal, J., et al. Modelling and Simulation for Autonomous Systems. MESAS 2023. Lecture Notes in Computer Science, vol 14615. Springer, Cham. https://doi.org/10.1007/978-3-031-71397-2_15
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DOI: https://doi.org/10.1007/978-3-031-71397-2_15
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