  In particle image velocimetry (PIV) the measurement signal is contained in
the recorded intensity of the particle image pattern superimposed on a variety
of noise sources. The signal-to-noise-ratio (SNR) strength governs the
resulting PIV cross correlation and ultimately the accuracy and uncertainty of
the resulting PIV measurement. Hence we posit that correlation SNR metrics
calculated from the correlation plane can be used to quantify the quality of
the correlation and the resulting uncertainty of an individual measurement. In
this paper we present a framework for evaluating the correlation SNR using a
set of different metrics, which in turn are used to develop models for
uncertainty estimation. The SNR metrics and corresponding models presented
herein are expanded to be applicable to both standard and filtered
correlations. In addition, the notion of a valid measurement is redefined with
respect to the correlation peak width in order to be consistent with
uncertainty quantification principles and distinct from an outlier measurement.
Finally the type and significance of the error distribution function is
investigated. These advancements lead to robust uncertainty estimation models,
which are tested against both synthetic benchmark data as well as actual
experimental measurements. For all cases considered here, standard
uncertainties are estimated at the 68.5% confidence level while expanded
uncertainties are estimated at 95% confidence level. For all cases the
resulting calculated coverage factors approximate the expected theoretical
values thus demonstrating the applicability of these new models for estimation
of uncertainty for individual PIV measurements.
