A Deceptive Jamming Template Synthesis Method for SAR Using Generative Adversarial Nets

In this paper, a deceptive jamming template generative adversarial network (DJTGAN) is proposed, which can intelligently generate high-fidelity deceptive jamming template matched with the practical SAR scenario. The DJTGAN consists of a deceptive jamming template generative network and a discriminative network. The generative network combines low-frequency content and high-frequency details of the target, and the discriminative network adopts PatchGAN architecture to capture local texture statistics to improve the fidelity of the deceptive jamming template. The MSTAR dataset is utilized to verify the effectiveness of the proposed DJTGAN. Moreover, the strip SAR deceptive jamming experiment based on the deceptive jamming templates generated by DJTGAN is done to further validate the effectiveness of the DJTGAN.