Custom Code Implementation - Wigner-Ville Distribution Algorithm

In this article, I'm sharing my self-developed program implementing the Wigner-Ville Distribution (WVD) algorithm. This program performs sophisticated time-frequency analysis on signals, utilizing a quadratic time-frequency representation that offers superior resolution compared to traditional Short-Time Fourier Transform methods. The core implementation employs a discrete WVD computation using a sliding window approach with bilinear transformation. Key algorithmic features include handling of cross-term interference through appropriate filtering techniques and optimized computation of the instantaneous autocorrelation function. The code structure efficiently manages signal segmentation and Fourier transformation operations to generate high-resolution time-frequency distributions. I've incorporated several enhancements including cross-term reduction algorithms and computational efficiency optimizations for large datasets. The program supports both analytic signals and real-valued signals with proper preprocessing. I welcome collaboration and knowledge exchange regarding this implementation. Please feel free to reach out with any technical questions, improvement suggestions, or performance optimization ideas. I look forward to engaging discussions about signal processing algorithms and encourage others to share their custom-developed programs or useful analytical tools as well.