Improved estimator for the slotted autocorrelation function of randomly sampled LDA data

The estimation of turbulence power spectra from randomly sampled laser Dopp ler anemometer (LDA) data can be done via the autocorrelation function (ACF ) approach, whereby the slotting technique has the advantage that the ACF c an be estimated at any data rate. Two improvements on Mayo's slotting techn ique for estimating the ACE 'local normalization' and the 'fuzzy slotting t echnique', were proposed and compared in a benchmark test. However, it prov ed possible to merge these approaches and the resulting algorithm produced correlation coefficients with a lower variance than either of the individua l algorithms. This lower variance in the ACF estimates can then be capitali zed upon in order to produce better estimates of the turbulence power spect rum.