Experimental Evaluation of the Maximum Likelihood Estimator Filter for Lagrangian Particle Tracking in Grid-Generated Turbulence

Accurate measurements of tracer particle positions are essential for identifying key features in turbulent flows during Lagrangian particle tracking. Despite robust tracking systems, systematic errors from various sources are inevitable. These errors can be reduced using filtering techniques. This work evaluates the performance of the Maximum Likelihood Estimator (MLE) filter developed by Kearney et al. (Experiments in Fluids, 2024, 65:24) using data from water tunnel experiments with grid-generated turbulence at a Reynolds number (Re_M) on the order of 10^3. The MLE filter is compared to spline filters by Gesemann (arXiv:1510.09034, 2015) and Gaussian filters by Mordant et al. (Physica D, 193(1):245–251, 2004). The MLE filter minimizes measurement errors and noise by incorporating stochastic process physics, assuming a Gaussian distribution for acceleration differences. Our objective is to experimentally determine the probability density function (PDF) of these acceleration differences as a function of Re_M and separation time. This will inform the iterative process and refine our assumption of the acceleration distribution. Understanding these differences will improve particle tracking accuracy and enhance the reliability of turbulence statistics, ultimately leading to better insights and advancements in fluid dynamics research.