This effort explores novel sub-resolution particle center estimation algorithms for Digital Particle Tracking Velocimetry (DPIV). The errors of these new methods were classified through Monte-Carlo simulations. These schemes provide direct measurements of the apparent particle image diameter and the subpixel position. The new methods significantly reduce the bias error due to pixel discretization, thus reducing the total error in the position and sizing measurement compared to the classic three point and least squares Gaussian estimators. In addition, the accuracy of the least-squares fits were essentially independent of the true particle diameter and significantly reduced the particle position error compared with current estimation schemes. The results of the Monte Carlo simulations were validated in a high pressure spray atomization experiment. / Master of Science