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An improved G-music algorithm for non-Gaussian noise condition direction-of-arrival estimation

Direction of arrival (DOA) estimation is one of the most important and widely used discussions within communication and radar systems. This paper aims to improve the DOA estimation using G-MUSIC (Multiple Signal Classification based on G-estimation) algorithm under noise types with heavy-tailed distributions such as impulsive noise conditions. Subspace-based DOA estimation methods, usually employ the maximum likelihood estimation of the covariance matrix and its eigenvalues and eigenvectors. However, the performance of this estimation and resulting the direction-of-arrival estimation degrade in non-Gaussian noise. In this paper we use the convex optimization methods to improve the DOA estimation algorithm, G-MUSIC, by modifying the eigenvector and eigenvalue estimation of the sample covariance matrix under non-Gaussian noise conditions. Simulation results confirm this performance improvement.