Time-Frequency Distribution Processing in MATLAB with Complete Implementation

The following presents the complete MATLAB implementation for time-frequency distribution processing. This code performs comprehensive time-frequency analysis on various types of signals using multiple signal processing techniques. The implementation begins by employing the Fast Fourier Transform (FFT) algorithm to convert time-domain signals into frequency-domain representations, utilizing MATLAB's built-in fft() function with proper zero-padding for optimal spectral resolution. The code includes a sophisticated preprocessing stage that applies various window functions to enhance time-frequency resolution. The implementation supports multiple window types: Hanning window (using hanning()), Blackman window (blackman()), Hamming window (hamming()), and rectangular window (rectwin()). Each window function is strategically applied to reduce spectral leakage and improve frequency localization. For time-frequency analysis, the code implements two primary distribution methods: Wigner-Ville Distribution (WVD) for high-resolution analysis and Cohen's class distributions for reduced cross-term interference. The WVD implementation calculates the instantaneous auto-correlation function followed by Fourier transformation, while Cohen's class incorporates smoothing kernels to suppress interference components. This comprehensive approach enables researchers to effectively analyze time-frequency characteristics of diverse signals and perform accurate signal processing tasks, with clear visualization of time-frequency representations using MATLAB's plotting capabilities.