tfrpwv Computes the Wigner-Ville Distribution for Discrete Signal X

In signal processing, tfrpwv is commonly used to compute the Wigner-Ville distribution for discrete signal X. This distribution serves as a time-frequency analysis method that can determine a signal's instantaneous frequency and amplitude information. The implementation typically involves a quadratic time-frequency distribution algorithm that uses the Fourier transform of the signal's autocorrelation function. However, due to the Wigner-Ville distribution's inherent issue with excessive cross-terms (interference components between different signal elements), practitioners often prefer using the smoothed pseudo Wigner-Ville distribution (tfrspwv) to compute signal X's time-frequency information. The tfrspwv function applies smoothing windows to both time and frequency domains, effectively reducing cross-term interference through separable kernel functions. This enhancement better reflects the signal's true time-frequency characteristics by suppressing artifacts while maintaining resolution. Overall, in signal processing applications, both tfrpwv and tfrspwv represent fundamental time-frequency analysis methods commonly implemented in MATLAB or Python signal processing toolboxes for investigating signal time-frequency properties.