Iterative root-MUSIC algorithm for DOA estimation

This paper introduces a new high-resolution subspace-based algorithm for direction-of-arrival estimation. The proposed method improves the quality of the estimation especially in the case of small sample size by considering the structure of the sample covariance matrix. The key idea is to identify undesirable terms in the sample covariance matrix which cause perturbations in the estimation of the signal and noise subspaces. These terms are then diminished in an iterative manner. The proposed method is studied by investigating the mean squared error, the detection probability, and the mean squared error in case of successful detection. It is shown that the new method outperforms the conventional methods.