Spectral Estimation Using MUSIC Algorithm and Improved MUSIC Method with Code Implementation

In our implementation, we employ both the standard MUSIC (Multiple Signal Classification) algorithm and an enhanced MUSIC method for signal spectral estimation. The MUSIC algorithm is a high-resolution spectral estimation technique based on signal subspace decomposition. This method separates the signal space into signal subspace and noise subspace through eigenvalue decomposition of the autocorrelation matrix. By processing the signal subspace using orthogonality principles between signal and noise subspaces, high-precision spectral estimates can be obtained through peak detection in the MUSIC pseudospectrum.

The improved MUSIC method builds upon the traditional approach by incorporating preprocessing steps to enhance estimation accuracy. Specifically, we implement signal smoothing techniques and noise filtering algorithms before subspace decomposition. The code includes forward-backward averaging for covariance matrix estimation and diagonal loading for numerical stability. These preprocessing steps effectively mitigate noise interference and improve the algorithm's robustness in practical implementations.

In the following sections, we will provide detailed explanations of both the standard MUSIC algorithm's underlying principles and the implementation specifics of the enhanced version, including key MATLAB functions for covariance matrix computation, eigenvalue decomposition using SVD (Singular Value Decomposition), and pseudospectrum calculation.