Conference Paper

A direct spectral estimation method for laser Doppler data using quantization of arrival times
Damaschke, N.; Kühn, V.; Nobach, H.
Proc. of the 19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics, July 16-19, 2018, Lisbon, Portugal
Abstract

This paper presents a method for estimating the autocorrelation function and the power spectral density from laser Doppler data with discretized arrival times. The method can be realized by direct estimation of the correlation function or by direct spectral estimation with further transformations to allow appropriate normalization including corrections for some deviations from the ideal Poisson sampling process like processor dead times. The method also makes use of processing steps, some of them initially developed for other estimation methods, like sample weighting, treatment of self-products, the above mentioned normalization or an effective reduction of the spectral resolution with most efficient use of information available. An example application on publicly available laser Doppler data shows agreement between the results obtained with competing methods. Furthermore, under this fair comparison, some methods converge in terms of their systematic and random errors, indicating that they are comparably efficient at using the available information content of the randomly sampled signal. The results also identify that the available methods are interchangeable and indicate a possible replacement for the current best- practice procedure in the laser Doppler community.


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Document Update (Table 3)
aslot.py - slotting estimator (code as used for the publication, may have bugs, which have been found later)
aint.py - interpolation method (code as used for the publication, may have bugs, which have been found later)
adir.py - direct estimator (code as used for the publication, may have bugs, which have been found later)
aquant.py - estimator with arrival time quantization (code as used for the publication, may have bugs, which have been found later)
pyLDV - more Python programs for calculating correlation functions and the appropriate power spectral densities from irregularly sampled LDV data sets (persistent bug fixes)
More about LDV Data Processing (incl. Statistical Bias, Weighting Schemes, Turbulence Spectra, Correlation Functions)
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