A Deceptive Jamming Technology Against SAR Based on Optical-to-SAR Template Translation

Deceptive jamming techniques against Synthetic Aperture Radar (SAR) are crucial for defending against hostile SAR reconnaissance and safeguarding our sensitive targets and regions. The current library of deceptive jamming templates for SAR is severely insufficient. The acquisition of measured and simulated SAR target images, which are key sources for the SAR deceptive jamming template library, can be problematic due to their lengthy acquisition period and high cost. In contrast, emulational optical target images are easier to generate. To address the challenge of quickly translating rich optical target images into SAR target deceptive jamming templates of diverse poses and high fidelity, this paper proposes a SAR target deceptive jamming template generation method based on Optical-to-SAR Cycle-consistent Adversarial Networks (OTSCycGAN). Firstly, this method utilizes deformable convolutional layers to partially replace the vanilla convolutional layers in the generator, enhancing the adaptive extraction ability of geometric transformation features between samples with varying azimuth angles, elevation angles, and target types in the dataset. Furthermore, a convolution kernel attention mechanism is introduced to dynamically adjust the receptive field sizes, increasing sensitivity for various target scales. Finally, we employ a loss function that incorporates Wasserstein GAN-Gradient Penalty (WGAN-GP) loss, hybrid L1-L2 loss, and focal frequency loss to advance the image quality at the pixel level. Our experiments on the constructed Target-Optical/SAR dataset demonstrate that this method brings a significant performance boost in SAR deceptive jamming template generation. The results of the stripmap SAR deceptive jamming, using deceptive jamming templates generated by OTSCycGAN, further confirm their effectiveness.