Power Spectrum Estimation with MATLAB Implementation

In this text, I would like to introduce several MATLAB routines for power spectrum estimation. Power spectrum estimation is a method used to analyze the spectral characteristics of signals, allowing researchers to understand signal frequency components and energy distribution through spectral analysis. MATLAB provides numerous built-in routines for power spectrum estimation that incorporate various algorithms, including Fourier transform methods, periodogram approaches, and auto-correlation techniques. These routines typically utilize key functions such as pwelch() for Welch's method, periodogram() for basic spectral estimation, and burg() for autoregressive modeling. The implementation generally involves specifying parameters like window type, sampling frequency, and overlap percentage to optimize spectral resolution and variance. Using these routines is straightforward - users simply input signal data and corresponding parameters to perform power spectrum estimation and generate corresponding spectrograms. For researchers and engineers working on power spectrum estimation, these MATLAB routines offer significant advantages through their optimized algorithm implementations and visualization capabilities, providing substantial assistance in signal processing applications.