Suppression of False-terms in Wigner-Ville Distribution using Time and Frequency Windowing

The Wigner-Ville distribution (WVD) is a widely used tool in the time-frequency analysis of non-stationary signals. However, the presence of false-terms in WVD for multicomponent signals can limit its applicability and interpretation. Various kernel and window-based smoothing methods have been used to remove false-terms from WVD, but they often come at the cost of reduced time-frequency resolution of autoterms. This paper proposes a novel sliding time and frequency windowing-based technique for removing false-terms from WVD, which aims to overcome the limitations of kernel-based methods. The proposed method segments a multi-component signal using overlapping windows in time and frequency domains successively and the WVD of each windowed signal is computed. The WVDs of all windowed signals are added together to obtain the falseterm free WVD. Energy scaling is also applied to minimize the effect of overlapping windows. Performance of the proposed method is evaluated for different multi-component synthetic signals and a natural ECG signal using various performance measures. The simulation results demonstrate that the proposed method can effectively remove false-terms from the WVD with improved auto-term enhancement and time-frequency resolution. Results from the proposed method are also compared with different kernel and window-based smoothing methods to show its superiority over these methods.