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.
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