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 si...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.展开更多
基金Supported by the National Natural Science Foundation of China(62201171).
文摘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.
