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In a combat environment, synthetic aperture radar (SAR) is attractive for several reasons, including automatic target recognition (ATR). In order to effectively develop ATR algorithms, data from a wide variety of targets in different configurations is necessary. Naturally, collecting all this data is expensive and time-consuming. To mitigate the cost, simulated SAR data can be used to supplement real data, but the accuracy and performance is degraded. We investigate the use of generative adversarial networks (GANs), a recent development in the field of machine learning, to make simulated data more realistic and therefore better suited to develop ATR algorithms for real-world scenarios. This class of machine learning algorithms has been shown to have good performance in image translation between image domains, making it a promising method for improving the realism of simulated SAR data. We compare the use of two different GAN architectures to perform this task. Data from the publicly available MSTAR dataset is paired with simulated data of the same targets and used to train each GAN. The resulting images are evaluated for realism and the ability to retain target class. We show the results of these tests and make recommendations for using this technique to inexpensively augment SAR data sets.
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