Efficient methods of estimate correlation functions of Gaussian processes and their performance analysis

Abstract

New efficient methods to estimate crosscorrelation functions of Gaussian signals are studied. In these methods, the "covariance property" of the Gaussian distribution is utilized such that the correlation estimates can be computed with only additions. To evaluate the performances of the methods, exact expressions for the bias and variance of these estimators are formulated and utilized in comparing these methods with the conventional correlation estimator. As a result, we point out that these new methods can give estimates which are comparable to the conventional approach.