Empirical and high-fidelity computational target models have routinely been inserted into synthetic imagery under conditions in which they were not created. Data collects are expensive and unable to be performed for all times and under all conditions. High-fidelity computational models can be time consuming and require engineers with specific expertise to generate. Therefore, instead of collecting or generating new data and models, previously generated models are used, often with hand-waving arguments that the model is close enough. DEVCOM Aviation and Missile Center has developed a tool to fill in the gap, using a physics-based calibrated model based on either the empirical or high-fidelity models to more easily and rapidly generate a new model under the specific scenario under investigation. this tool has also been used to help generate large numbers of target synthetic imagery for help in training AI algorithms.

Many AI/ML training datasets used for military algorithm development lack the necessary diversity to span typical operating conditions. Typical workflows for augmenting datasets with synthetic data require cumbersome setups and slow runtimes. To address the need for rapid augmentation of datasets, DEVCOM AvMC has developed a suite of tools that can be independently modulated to allow for the rapid generation of diverse training data. This paper will outline the key products that allow this rapid generation capability and share results demonstrating the capability.