Enhancing particle image tracking performance with a sequential Monte Carlo method: The bootstrap filter

Abstract

One of the common problems of current pattern match and particle image tracking algorithms is the deployment of constant velocity assumption for particle motion between two frames, which would result in serious errors when high velocity gradient flows are measured. To address this issue, a new particle image tracking method—bootstrap filter tracking is proposed. In this new method, a simple nonlinear dynamic model which takes particle acceleration into account is employed and a sequential Monte Carlo method—bootstrap filter is used in conjunction with pattern match algorithm to strengthen the particle image tracking performance. By using the nonlinear system model and bootstrap filter, particle location at next time step can be predicted accurately and the new method is able to measure high velocity gradient flows with better performance than the traditional particle image tracking algorithms. This new method is validated by using numerically generated particle images. Its accuracy in terms of particle image density, out-of-plane displacement and displacement gradient is compared with the Kalman filter tracking (Takehara et al., 2000 [34]) and the Super-PIV (Keane et al., 1995 [30]) methods. The three algorithms are also compared by using a set of real turbulent jet images. The test results demonstrate that the bootstrap filter tracking method is superior than the Kalman filter tracking and the Super-PIV methods for measuring low density, high velocity gradient flows.