WVD-GAN: A Wigner-Ville distribution enhancement method based on generative adversarial network

Abstract

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.