Local time estimation for the slotted correlation function of randomly sampled LDA data

Abstract

 The task of autocorrelation and power spectral density estimation from velocity data sampled irregularly in time by a laser-Doppler anemometer (LDA) is addressed in this article. A new method based on the slotting technique was found to be a very reliable estimator. This article describes specific improvements of the slotting technique, the model-based variance estimation and the spectral transform leading to more accurate estimates of the autocorrelation function and the power spectral density. Furthermore, the new method yields more information especially at short time lags of the autocorrelation function, which can be used to derive improved estimates of the Taylor time scale.