Abstract: Augmenting samples of single Synthetic Aperture Radar (SAR) deception jamming templates to generate a high-quality SAR deception jamming template library facilitates rapid and effective SAR deception jamming. Currently, the sample augmentation scheme for SAR deception jamming templates lacks coherent speckle noise, resulting in lower authenticity of generated templates, and simultaneously, lower similarity between generated template images and original images. To address this issue, this paper proposes a sample augmentation scheme based on Generative Adversarial Networks (GANs), considering the influence of coherent speckle noise within the network, and utilizing attention mechanism modules, residual dense mod-ules, and multi-scale modules to enhance the network's feature extraction capability. Experiments on the MSTAR dataset demonstrate that the images generated by this scheme exhibit more similar image features to the original images and contain similar coherent speckle noise features, thereby confirming the effectiveness of the proposed method.