New Bayesian Estimation Method Based on Particle Flow Velocity

Aim: ing at the state estimation problem of non-linear systems (NLSs), the traditional typical nonlinear filtering methods (e.g., Particle Filter, PF) have large errors in system state, resulting in low accuracy and high computational speed. To perfect the imperfections, a new Bayesian estimation method based on particle flow velocity (PFV-BEM) is proposed in this paper. Firstly, the state update is carried out according to the projection principle to calculate the prior information of the state and select its particle points. Secondly, the particle flow velocity defined, which describes the evolution process of random samples from the prior distribution to the posterior distribution. The posterior information of the state is calculated by solving the parameters related the particle flow velocity. Finally, the estimated mean and standard deviation of the state are solved. Simulation experiments use one-dimensional general nonlinear examples and multi-target motion tracking as examples, it is compared with PF, the simulation results show the feasibility of the novel Bayesian estimation algorithm.