ISAR Images Super-Resolution Method Based on Ameliorated CycleGAN

‍ ‍For deception jamming against ISAR， it is difficult to generate high-fidelity fake targets due to the lack of model samples. To address this problem， image super-resolution achieved by end-to-end mapping learned by generative adversarial network can be employed. However， when the resolutions of the training set and the test set are quite different， fake scattering points will appear in the super-resolution results，which may led to the target distortion. In this paper， we propose a super-resolution sample generation method based on ameliorated CycleGAN due to the robustness of CycleGAN to the diversity of input samples. Compared with the original CycleGAN， we modify loss function， optimization process and framework of discriminator， which speeds up the convergence and enhances the network’s generalization performance for different resolution input ISAR images. Relevant experiments using data tested in anechoic chamber demonstrate the effectiveness and superiority of our method compared to GAN. Especially when the resolution of the training and test sets are very different， super-resolution image generated by our method has more matching scattering points with ground truth.