Abstract: The precision of dynamic precise point positioning using Kalman filtering will be degraded,even be divergent when the outliers exist.Particle filtering is applied to control the influences of the observational outliers,and improve the accuracy of positioning.Particle filtering is a kind of nonlinear filter with non-Gaussian distribution,and it can obtain accurate parameters by random sample.The weight of each particle is defined based on the probability densities of the observational errors,predicted state errors as well as the important distribution in order to control the influences of contaminated particles to the positioning results.Kalman filtering is employed to get the important sampling to slow down the degeneracy of the particle.The free-ionosphere ambiguities are fixed before data processing to reduce the number of parameters in the state vector.An actual dynamic GPS data set is employed to test the particle filter procedure.The procedure of the particle filtering can efficiently control the influences of the observational outliers,and improve the accuracy of the dynamic precise point positioning.