Adaptive Masked Wigner–Ville Distribution Network

Wigner-Ville Distribution (WVD) is a commonly used time-frequency representation (TFR) tool for analyzing amplitude-modulated and frequency-modulated (AM-FM) signals. Unlike the linear-based time-frequency (TF) methods, WVD is quadratic and not limited by the Heisenberg uncertainty principle, enabling high-resolution time-frequency spectra. Nevertheless, traditional WVD suffers from the presence of cross-terms, which restricts its practical applications. To address this issue, we propose an adaptive masked Wigner-Ville Distribution network (AMWVD-Net), an interpretable unfolding-based learning approach that combines iterative optimization techniques with deep neural networks. This hybrid design leverages the prior knowledge embedded in optimization algorithms while benefiting from the learning capacity and hardware acceleration of deep learning. Moreover, it avoids the parameters that need to be set manually in the traditional optimization algorithms. Due to its efficient deep learning architecture, our AMWVD-Net can utilize hardware acceleration and parallel acceleration, thereby avoiding the computational inefficiency issue of traditional iterative algorithms while maintaining good TF results. To test the effectiveness of the proposed model, we apply it to synthetic and real data. The numerical results demonstrate that our suggested AMWVD-Net achieves superior time-frequency resolution while significantly reducing computational time compared to conventional iterative optimization-based WVD methods.