Adaptive WVD Cross-Term Removal Method Based on Multidimensional Property Differences

Wigner–Ville distribution (WVD) is widely used in the field of signal processing due to its excellent time–frequency (TF) concentration. However, WVD is severely limited by the cross-term when working with multicomponent signals. In this paper, we analyze the property differences between auto-term and cross-term in the one-dimensional sequence and the two-dimensional plane and approximate entropy and Rényi entropy are employed to describe them, respectively. Based on this information, we propose a new method to achieve adaptive cross-term removal by combining seeded region growing. Compared to other methods, the new method can achieve cross-term removal without decreasing the TF concentration of the auto-term. Simulation and experimental data processing results show that the method is adaptive and is not constrained by the type or distribution of signals. And it performs well in low signal-to-noise ratio environments.