Sea Clutter Data Augmentation Method Based on Deep Generative Adversarial Network
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摘要: 海杂波数据稀缺，获取海杂波数据成本高、周期长，极大地限制了海杂波特性研究及海洋遥感应用。该文主要研究了基于深度生成性对抗网络(GAN)的海杂波数据生成方法，通过扩展传统的GAN框架，形成了1维海杂波数据生成和鉴别模型，基于实测海杂波数据集，进行对抗网络生成和鉴别模型训练，分析了生成模型所生成的海杂波数据的幅度分布特性和时间、空间相关性。基于实测数据验证了该方法能够生成更多、更多样、与真实海杂波数据分布相近的海杂波数据。Abstract: Due to the scarcity of sea clutter data, the high cost and long period of obtaining sea clutter data greatly limit the research of sea clutter characteristics and the application of ocean remote sensing. The method of sea clutter data generation based on the Generative Adversarial Networks (GAN) is studied. By extending the traditional GAN framework, a one-dimensional sea clutter data generation and identification model is formed. Based on the radar measured sea clutter data set, the generation and identification model training in the adversarial network is carried out. The amplitude distribution characteristics and time and spatial correlation of the sea clutter data generated by the model are analyzed. Based on the measured data, it is verified that the method can generate more sea clutter data with more variety, and similar distribution to the real sea clutter data.
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表 1 生成器、辨别器网络参数
生成器网络 判别器网络 Layer Act./Norm Output shape Layer Act./Norm Output shape Fully Linear ReLU Conv1d Leaky ReLU 64×2048 BatchNorm1d 1×256 Conv1d Leaky ReLU 128×512 Conv1d ReLU 512×512 Conv1d Leaky ReLU 256×128 Conv1d ReLU 256×1024 Conv1d Leaky ReLU 512×128 Conv1d ReLU 128×1024 Conv1d Leaky ReLU 1024×16 Conv1d ReLU 64×4096 Fully Linear sigmoid 1×1 Conv1d Tanh 1×8192 
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