Time-frequency analysis based on Wigner-Ville distribution (WVD) plays a significant role in analysing non-stationary signals, but it is susceptible to interference from cross-terms (CTs) for multi-component signals. To address this issue, a novel WVD enhancement method based on generative adversarial networks (namely WVD-GAN) is proposed, to achieve highly-concentrated time-frequency (TF) representation. Specifically, a deep feature extraction module is designed with multiple residual connections in the generator of WVD-GAN to leverage the latent information encoded in the shallow representations. Meanwhile, a simple and effective attention module is introduced to enhance auto-term features. Moreover, a multi-scale discriminator is proposed based on dilated convolutions to guide the generator to reconstruct high-resolution TF images by discriminating CT. Finally, a comparative analysis is provided to demonstrate the effectiveness and robustness of the proposed method on different simulated and real-life datasets. Extensive experiments demonstrate that the proposed method outperforms several state-of-the-art methods.

中文翻译：

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WVD-GAN：一种基于生成对抗网络的 Wigner-Ville 分布增强方法

基于维格纳-维尔分布（WVD）的时频分析在非平稳信号分析中发挥着重要作用，但它容易受到多分量信号交叉项（CT）的干扰。为了解决这个问题，提出了一种基于生成对抗网络（即WVD-GAN）的新型WVD增强方法，以实现高度集中的时频（TF）表示。具体来说，在 WVD-GAN 的生成器中设计了一个具有多个残差连接的深层特征提取模块，以利用浅层表示中编码的潜在信息。同时，引入了一个简单有效的注意力模块来增强自动术语功能。此外，提出了一种基于扩张卷积的多尺度判别器，以指导生成器通过判别 CT 来重建高分辨率 TF 图像。最后，提供了比较分析，以证明所提出的方法在不同的模拟和现实数据集上的有效性和鲁棒性。大量的实验表明，所提出的方法优于几种最先进的方法。