Spectral Estimation Methods: (1) Modified Covariance Method, (2) Multiple Signal Classification (MUSIC) Algorithm, (3) ESPRIT Algorithm, (4) Pisarenko Harmonic Decomposition Method

This expanded technical document provides detailed coverage of spectral estimation methodologies while preserving core concepts. The paper systematically examines four advanced spectral analysis techniques: the Modified Covariance Method for improved autocorrelation estimation, the MUSIC algorithm employing noise subspace orthogonalization, the ESPRIT method utilizing rotational invariance properties, and Pisarenko's Harmonic Decomposition for frequency component extraction. Each method includes MATLAB implementation code featuring key functions like eig() for eigenvalue decomposition, svd() for singular value analysis, and corrmtx() for correlation matrix computation. The algorithms are structured with proper signal preprocessing steps and parameter configuration sections. Users can extract the files and execute the complete MATLAB implementation, which contains commented code explaining the computational flow of each spectral estimation approach, including array processing configurations for multi-signal scenarios and performance comparison modules for analytical evaluation.