MUSIC Algorithm Source Code
The MUSIC algorithm is a subspace decomposition method that separates the observation space into signal and noise subspaces. From a geometric perspective, these two subspaces are orthogonal, where the signal subspace comprises eigenvectors corresponding to signals in the data covariance matrix, while the noise subspace contains eigenvectors associated with the smallest eigenvalues (noise variance). This implementation utilizes Python/NumPy routines for covariance matrix computation, eigenvalue decomposition via numpy.linalg.eig, and pseudospectrum construction through noise subspace vectors. The algorithm achieves high-resolution direction-of-arrival estimation compatible with arbitrary array geometries.