MATLAB Program for Power Spectrum Calculation with Multiple Algorithm Implementations

This document provides MATLAB implementations of multiple power spectrum calculation algorithms. The collection includes Autocorrelation method for basic spectral estimation through signal self-correlation computation, MUSIC (Multiple Signal Classification) algorithm employing signal subspace decomposition for high-resolution frequency estimation, Burg method based on autoregressive modeling for efficient spectral analysis, and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm utilizing signal subspace rotation invariance properties. Each implementation contains the core algorithmic components with proper parameter handling and visualization capabilities. The Autocorrelation method computes power spectrum via Fourier transform of the autocorrelation sequence. MUSIC algorithm implements eigenvalue decomposition of the covariance matrix to separate signal and noise subspaces. Burg method recursively estimates reflection coefficients using forward and backward prediction errors. ESPRIT algorithm employs rotational invariance between signal subspaces to estimate frequency components. These ready-to-use programs facilitate power spectrum analysis for signal processing research and applications, providing insights into signal frequency characteristics through different mathematical approaches. All codes include appropriate comments and example usage for straightforward implementation in spectral analysis projects.