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Correlation estimators based on simple nonlinear transformations

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2 Author(s)
Sullivan, M.C. ; Eng. Res. Associates, E-Systems Inc., Vienna, VA, USA ; Wegman, E.J.

The computational cost of estimating correlations may 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 real and complex Gaussian processes. With independent observations, the variance of the estimator is approximately 14% higher than that obtained by averaging lag products

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Signal Processing, IEEE Transactions on  (Volume:43 ,  Issue: 6 )