Improved MUSIC algorithm for high resolution angle estimation

• A set of spatial temporal correlation matrices are constructed.
• A uniform expression is established by the above matrices.
• A cyclic optimization algorithm is designed to calculate the signal subspace.
• Proposed algorithm possesses better subspace accuracy and RMSE than the MUSIC.
• Proposed algorithm has a higher resolution compared with the MUSIC algorithm.

By using a single correlation matrix, classic MUSIC algorithm estimates subspaces through traditional eigenvalue decomposition. Its performances suffered from these inaccurate subspaces greatly. In this paper, a set of spatial temporal correlation matrices are firstly constructed by exploiting the array received data. Secondly, in order to get more accurate subspace, we establish a uniform cost function that exploits these matrices. Thirdly, a cyclic optimization algorithm is designed to jointly estimate the signal subspace. Moreover, by the relation between signal and noise subspace, the corresponding projection matrix of noise subspace is obtained, hence an improved MUSIC algorithm is implemented by this projection matrix. Finally three experiments are conducted to validate the performances of the proposed algorithm.