  The design of high-resolution and cross-term (CT) free time-frequency
distributions (TFDs) has been an open problem. Classical kernel based methods
are limited by the trade-off between TFD resolution and CT suppression, even
under optimally derived parameters. To break the current limitation, we propose
a data-driven kernel learning model directly based on Wigner-Ville distribution
(WVD). The proposed kernel learning based TFD (KL-TFD) model includes several
stacked multi-channel learning convolutional kernels. Specifically, a skipping
operator is utilized to maintain correct information transmission, and a
weighted block is employed to exploit spatial and channel dependencies. These
two designs simultaneously achieve high TFD resolution and CT elimination.
Numerical experiments on both synthetic and real-world data confirm the
superiority of the proposed KL-TFD over traditional kernel function methods.
