Spectral analysis of biased LDA data

The spectral density function of turbulent velocity fluctuations can be est imated from randomly sampled LDA data by first computing a discretized auto correlation function (using the slotting technique) followed by a Fourier t ransform of this function. The spectral estimates obtained in this way have a large statistical scatter in the high-frequency range. This work focuses on a spectral estimator with a much reduced statistical scatter (approxima tely I decade), enabling the retrieval of the spectral density function up to higher frequencies. This spectral estimator is based on a modification o f the standard slotting technique, known as 'local scaling or 'local normal ization', in conjunction with a window of variable width. A series of bench mark tests for spectral estimators indicated that this estimator yields goo d overall results. This paper explores the characteristics of the new spect ral estimator with regard to the effects of velocity bias and the presence of uncorrelated noise in the velocity data.