Robust DOA Estimator Under Non-Gaussian Noise and Insufficient Sample Support

In this paper, we develop a robust algorithm for improving the accuracy of direction-of-arrival estimation under non-Gaussian noise and insufficient sample support. (The number of sensors is large, while the number of samples is relatively small.) Unlike the traditional peak-search techniques, our approach is based on an enhanced covariance matrix estimation, where we exploit the thoughts of the M-estimator and the shrinkage estimator, but devise a new target matrix equation and iterative solution procedure. Numerical results indicate that the proposed algorithm significantly performs better than the existing methods in the presence of non-Gaussian noise and finite samples.