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Artificial intelligence (AI) models are at the core of improving computer-assisted tasks such as object detection, target recognition, and mission planning. The development of AI models typically requires a large set of representative data, which can be difficult to acquire in the military domain. Challenges include uncertain and incomplete data, complex scenarios, and scarcity of historical or threat data. A promising alternative to real-world data is the use of simulated data for AI model training, but the gap between real and simulated data can impede effective transfer from synthetic to real-world scenarios. In this study, we provide an overview of the state-of-the-art methods for exploiting simulation data to train AI models for military applications. We identify specific simulation considerations and their effects on AI model performance, such as simulation variation and simulation fidelity. We investigate the importance of these aspects by showcasing three studies where simulated data is used to train AI models for military applications, namely vehicle detection, target classification and course of action support. In the first study, we focus on military vehicle detection in RGB images and study the effect of simulation variation and the combination of a large set of simulated data with few real samples. Subsequently, we address the topic of target classification in sonar imagery, investigating how to effectively integrate a small set of simulated objects into a large set of low-frequency synthetic aperture sonar data. We conclude with a study on mission planning, where we experiment with the fidelities of different aspects in our simulation environment, such as the level of realism in movement patterns. Our findings highlight the potential of using simulated data to train AI models, but also illustrate the need for further research on this topic in the military domain.
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