Enhancing ISAR Resolution by a Generative Adversarial Network

Abstract

Recent studies have shown the superiority of neural networks on imaging equality and efficiency in inverse synthetic aperture radar (ISAR) resolution enhancement, but a central problem remains largely unsolved: all recent studies based on neural networks focused on minimizing the mean-squared reconstruction error (MSE), causing limited enhancing factors and inaccurate recovery of weak point scatters. In order to address this problem, a framework based on a generative adversarial network (GAN) using a combined loss composed of the absolute loss and the adversarial loss is proposed in this letter. The absolute loss ensures that reconstructed high-resolution ISAR images achieve higher enhancing factors and lower sidelobes. The adversarial loss pushes this framework to recover accurate amplitude and position of weak point scatters by a discriminator that is trained to differentiate reconstructed high-resolution ISAR images and real high-resolution ISAR images. Compared to some state-of-the-art methods, our GAN-based framework provides superior reconstruction with higher enhancing factors and more target details.