Robust frequency estimation based on trimmed correlation in impulsive environments

The MUSIC method represents a class of super-resolution methods for frequency estimation. However, it has poor performance in impulsive noise environments due to the presence of outliers. A more robust method called trimmed correlation based-MUSIC (TR-MUSIC) method is proposed in this paper. Through a trimming operation, outliers in the samples participating in the correlation calculation are discarded. The amount of trimming is determined by the Mahalanobis distance in which robust estimates of location and scale are utilized. Frequency estimation results from the eigendecomposition of the trimmed correlation matrix. Corroborating simulations are presented to show the robustness and performance improvement of the proposed method.