Particle flow filter formulated the dynamics from prior samples with posterior samples with particle flow velocity field to perform Bayesian estimation of system state.To address difficulties of particle velocity field computation in present particle flow filter

a novel particle flow filter based on Gaussian assumption was proposed.The analytical solution of velocity field under linear Gaussian condition was derived.The consistency of this analytical solution with Kalman-Bucy filter for continuous system

when discrete dynamic step goes to zero

was proved.The solution was finally extended to obtain the nonlinear Gaussian velocity field expression which can be approximated by using unscented transformation.Several simulations revealed the effectiveness over classic nonlinear Gaussian assumption on accuracy and particle filter on efficiency and stability.