SPA-GAN: SAR Parametric Autofocusing Method with Generative Adversarial Network


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Traditional synthetic aperture radar (SAR) autofocusing methods are based on the point-scattering model, which assumes the scattering phases of a target to be a constant. However, as for the distributed target, especially the arc-scattering target, the scattering phase changes with the observation angles, i.e., its scattering phase is time-varying. Hence, the compensated phases are a mixture of the time-varying scattering phases and the motion error phases in the traditional autofocusing methods, which causes the distributed target to be overfocused as a point target. To solve the problem, in this paper, we propose a SAR parametric autofocusing method with generative adversarial network (SPA-GAN), which establishes a parametric autofocusing framework to obtain the correct focused SAR image of the distributed targets. First, to analyze the reason for the overfocused phenomenon of the distributed target, the parametric motion error model of the fundamental distributed target, i.e., the arc-scattering target, is established. Then, through estimating the target parameters from the defocused SAR image, SPA-GAN can separate the time-varying scattering phases from the motion error phases with the proposed parametric motion error model. Finally, by adopting the traditional autofocusing method directly, SPA-GAN can obtain the correct focused image. Extensive simulations and practical experiments are carried out to demonstrate the effectiveness of the proposed method.
SAR autofocusing,motion error compensation,parametric autofocusing,arc-scattering target,parameter estimation,generative adversarial network (GAN)
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