Given the limited amount of measured synthetic aperture radar data available to train object recognition algorithms. Synthetic data is used to train the algorithm while using measured data to test. To account for the variability of measured data and to ensure robustness to various conditions, extensive physics- based augmentations are used during the training process. These augmentations include target, background, and sensor variability. In order to explore the augmentation space most efficiently, the background and sensor variability are explored on-line during the training process using an adversarial learning strategy. Performance trades are reported as a function of the various augmentation strategies.
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