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

Abstract

AbstractTime‐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.