MATLAB Implementation of MUSIC Algorithm for DOA Estimation

The MUSIC (Multiple Signal Classification) algorithm is a fundamental technique in digital signal processing for simulating Direction of Arrival (DOA) measurement. DOA estimation is crucial for determining the spatial coordinates of signal sources in three-dimensional space. The MUSIC algorithm is particularly valued for its high-resolution capabilities in estimating DOA even under noisy conditions. The algorithm operates by first estimating the signal subspace through eigenvalue decomposition of the covariance matrix, then projecting the received signal onto the orthogonal complement of this subspace. This projection generates a spectrum-like function where peaks correspond to potential DOA estimates. In MATLAB implementation, key steps typically involve: - Computing the sample covariance matrix from received array data - Performing eigenvalue decomposition using built-in functions like 'eig' or 'svd' - Identifying noise subspace eigenvectors corresponding to smaller eigenvalues - Constructing the MUSIC pseudospectrum function using the noise subspace - Finding spectral peaks through search algorithms to determine DOA angles Practical implementations often utilize MATLAB's array processing toolbox functions and may incorporate techniques like forward-backward averaging for improved performance. The MUSIC algorithm finds extensive applications in radar systems, sonar technology, wireless communications, and array signal processing where accurate spatial source localization is required.