A novel method is introduced for increasing the accuracy and extending the dynamic range of time-resolved PIV. The approach extends the well-known concept of multiframe particle tracking velocimetry (PTV) to cross-correlation analysis employed by PIV. The working principle is based on the determination of fluid element trajectories by tracking their position across an image sequence. The fluid trajectory correlation (FTC) algorithm deals with the effect of trajectory curvature and non-uniform velocity during the considered time interval by allowing the motion within the trajectory to be nonlinear. In addition, the local image deformation accounts for the spatial velocity gradient and its change along the trajectory. The principle for reduction of the measurement error is threefold: by enlarging the temporal measurement interval, the relative error becomes smaller; secondly, the random error is reduced with the use of a least-squares polynomial fitting approach to the individual trajectory; and finally, the use of nonlinear fitting functions allows for a reduction in truncation errors. The evaluation of velocity proceeds then directly from the analytical derivatives of the least-squares functions. The principal features of this algorithm are compared with a single-pair iterative image deformation method through the use of synthetic image sequences depicting steady flows (solid body rotation and uniform motion), and with an application to an experimental data set of a submerged circular jet. 德國LaVision PIV/PLIF粒子成像測速場儀 自適應(yīng)粒子成像測速場儀（PIV） 時(shí)間分辨粒子成像測速系統(tǒng)(TR-PIV)