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Generative Adversarial Network for Radar Signal Generation

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Document pages: 7 pages

Abstract: A major obstacle in radar based methods for concealed object detection onhumans and seamless integration into security and access control system is thedifficulty in collecting high quality radar signal data. Generative adversarialnetworks (GAN) have shown promise in data generation application in the fieldsof image and audio processing. As such, this paper proposes the design of a GANfor application in radar signal generation. Data collected using theFinite-Difference Time-Domain (FDTD) method on three concealed object classes(no object, large object, and small object) were used as training data to traina GAN to generate radar signal samples for each class. The proposed GANgenerated radar signal data which was indistinguishable from the training databy qualitative human observers.

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