CV-SAGAN: Complex-valued Self-attention GAN on Radar Clutter Suppression and Target Detection

Yuanhang Wu, Chenyu Zhang, Yiru Lin, Xiaoxi Ma, Wei Yi
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Abstract

Traditional clutter suppression and target detection methods have limitations in that they must satisfy specific statistical models. In this paper, we propose a unified deep learning model for complex-valued self-attention generative adversarial networks (CV-SAGAN) for clutter suppression and target detection. In the complex-valued framework, we first use a generator module to learn the clutter distribution and perform clutter suppression. Then, a self-attention module is used for the first time to perform corrective detection of sparse targets. Finally, a discriminator is used to judge between the real data and the network output results, improving the robustness of the model. We verified that the CV-SAGAN model has a better detection rate and robustness than the conventional cell-average constant false alarm rate (CA-CFAR), real-valued GAN, and real-valued SAGAN on three clutter distributions and achieved better detection results on the publicly available IPIX real-world dataset.
CV-SAGAN:复值自关注GAN在雷达杂波抑制和目标检测中的应用
传统的杂波抑制和目标检测方法必须满足特定的统计模型,存在一定的局限性。在本文中,我们提出了一种用于杂波抑制和目标检测的复杂值自注意生成对抗网络(CV-SAGAN)的统一深度学习模型。在复值框架中,我们首先使用生成器模块来学习杂波分布并进行杂波抑制。然后,首次使用自关注模块对稀疏目标进行校正检测。最后,利用判别器对真实数据和网络输出结果进行判别,提高了模型的鲁棒性。我们验证了CV-SAGAN模型在三种杂波分布上比传统的细胞平均恒定虚警率(CA-CFAR)、实值GAN和实值SAGAN具有更好的检测率和鲁棒性,并且在公开可用的IPIX真实数据集上取得了更好的检测结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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