Obscured Object Training with GAN Augmented Synthetic Data

Machine learning algorithms have demonstrated performance on par with or superior to human analysts on large datasets when sufficient training data is available. For many military applications, sufficient truthed data over relevant operating conditions does not exist and is prohibitively expensive to obtain. A potential solution to thislack of measured datais synthetic data derived from physics simulations to both train and characterize the algorithm.However, to do so we must address the disconnect between the synthetic model and the operating conditions of the real worldmeasured data. This research will develop solutions to this problemutilizing high fidelity physics models, statistical clutter models, radiative transfer models (RTMs), and generative adversarial networks (GANs) to generate training data over a large range of operating conditions (OCs) for which few measured samples exist.