MUSIC Algorithm for Signal and Noise Subspace Separation using Covariance Matrix

The MUSIC algorithm separates signal and noise subspaces by eigen-decomposition of the received data covariance matrix (Rx). It constructs spatial scanning spectra by exploiting the orthogonality between signal steering vectors and noise subspace, then performs peak searching in the parameter domain for accurate signal parameter estimation. Implementation typically involves eigenvalue decomposition, subspace identification, and peak detection algorithms.