利用生成对抗网络的时频图像去噪和增强处理

针对雷达信号时频图像的去噪和增强问题,提出了利用生成对抗网络二次生成时频图像的方法。首先利用时频分析产生雷达信号的时频图像作为原始数据集1;接着利用生成对抗网络对数据集1进行学习之后生成新的数据集2,数据集2相对于数据集1拥有着去噪和增强的效果;最后提取时频图像奇异值特征检验生成的数据集2的有效性。对6种常见的雷达信号的时频图像进行了仿真实验,结果证明了该方法在时频图像去噪和增加样本多样性方面是有效的。

Time-Frequency Image De-noising and Enhancement Processing Based on Generative Adversarial Network

To deal with the problem of denoising and enhancement of radar signal time-frequency images,a method of secondarily generating time-frequency images by generative adversarial network is proposed.Firstly,time-frequency analysis is used to generate the time-frequency image of the radar signal as the original data set 1.Then,after learning the data set 1 by using the generative adversarial network,a new data set 2 is generated,and the data set 2 has denoising and enhancement effects relative to data set 1.Finally,the validity of the data set 2 generated by the time-frequency image singular value feature is checked.Experiments on the time-frequency images of six common radar signals are carried out.The results show that the method is effective in time-frequency image denoising and increasing sample diversity.