TR-MUSIC—A robust frequency estimation method in impulsive noise

Sinusoidal frequency estimation has been studied for many years. The MUSIC method represents a class of super-resolution methods based on subspace decomposition. However, the MUSIC method has poor performance in impulsive noise environments due to the prevalence of outliers and very large noise variance. 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, yielding a correlation sequence that is closer to the true underlying correlation. The amount of trimming is determined by the Mahalanobis distance in which robust estimates of location and scale are utilized to compensate for outlier effects. Frequency estimation results from the eigendecomposition of the trimmed correlation matrix. In the simulations, we take αα-stable noise (α>1α>1) as an example of impulsive noise. The proposed method is very robust and performs better than the conventional MUSIC and other robust methods. Furthermore, it can be applied to real signals as well as complex signals.