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

Abstract

The estimation of turbulence power spectra from randomly sampled laser Doppler anemometer (LDA) data can be done via the autocorrelation function (ACF) approach, whereby the slotting technique has the advantage that the ACF can be estimated at any data rate. Two improvements on Mayo's slotting technique for estimating the ACF, `local normalization' and the `fuzzy slotting technique', were proposed and compared in a benchmark test. However, it proved possible to merge these approaches and the resulting algorithm produced correlation coefficients with a lower variance than either of the individual algorithms. This lower variance in the ACF estimates can then be capitalized upon in order to produce better estimates of the turbulence power spectrum.