  We investigate sources of error in acceleration statistics from Lagrangian
Particle Tracking (LPT) data and demonstrate techniques to eliminate or
minimise bias errors introduced during processing. Numerical simulations of
particle tracking experiments in isotropic turbulence show that the main
sources of bias error arise from noise due to position uncertainty and
selection biases introduced during numerical differentiation. We outline the
use of independent measurements and filtering schemes to eliminate these
biases. Moreover, we test the validity of our approach in estimating the
statistical moments and probability densities of the Lagrangian acceleration.
Finally, we apply these techniques to experimental particle tracking data and
demonstrate their validity in practice with comparisons to available data from
literature. The general approach, which is not limited to acceleration
statistics, can be applied with as few as two cameras and permits a substantial
reduction in the spatial resolution and sampling rate required to adequately
measure statistics of Lagrangian acceleration.
