A normalized correlation estimator for complex data based on a quadruplex transformation

The computational cost of estimating normalized correlations can be reduced by employing sums of simple nonlinear functions of the data. A quadruplex transformation is presented, and the performance of the associated estimator is analyzed for complex Gaussian processes. With independent observations, the variance of the estimator is approximately 14% higher than that of the conventional estimator.