Implementing Time-Frequency Analysis of Digital Signals Using the Wigner Method

The Wigner method is employed to perform time-frequency analysis on digital signals. As a widely-used signal processing technique, the Wigner distribution enables simultaneous analysis of signals in both time and frequency domains, capturing their time-varying spectral characteristics. This method finds applications across various domains including communications, audio processing, and image analysis. Through implementation of the Wigner method, precise time-frequency analysis can be conducted on digital signals, facilitating extraction of key signal features such as instantaneous frequency components and energy distribution patterns. The core algorithm involves calculating the Fourier transform of the signal's autocorrelation function, typically implemented using discrete Fourier transforms (DFT) or fast Fourier transforms (FFT) in practical applications. Key computational considerations include handling cross-term interference in multi-component signals through techniques like pseudo-Wigner distributions or smoothing kernels. This time-frequency analysis approach provides valuable insights for understanding and processing digital signals in complex scenarios.