Model parameter estimation from non-equidistant sampled data sets at low data rates

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Abstract
A general method of model parameter estimation for irregularly sampled data is introduced, with special emphasis on estimation of the power spectral density. The main application is processing of data sets from a laser Doppler anemometer (LDA), for which often the mean data rate is low and the total data set duration is short. It is shown that the model parameter estimation can be quite effective under these conditions, resulting in consistent, bias-free estimates which exhibit very low variance. Simulations and experiments are used to examine the performance of the technique.