A Learnable Radar Imaging Paradigm Driven by Deep Generative Model

Abstract

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The improvement of synthetic aperture radar (SAR) image quality is the constant subject of SAR technology. In previous works, certain physical models were built to form SAR images from raw echoes, such as Range-Doppler algorithm (RDA). Although this family of methods is effective in the ideal conditions, the generalization ability is poor under the special scenarios. Moreover, these methods ignore the large amounts of history SAR data. And their processing speed is not enough to support their application in real-time systems unless draconian restrictions are added. To solve these problems, a learnable radar imaging paradigm driven by deep generative model is proposed in this paper. A deep fully convolutional network is first developed to achieve the mapping from the raw echoes to the imaging results. Then a large amount of historical echo data and corresponding SAR images formed by RDA are used to train and evaluate this network. It has been proven that our imaging paradigm can quickly form high-quality imaging results in various scenarios, through simulations and experiments.