Two Stage Particle Filter for Nonlinear Bayesian Estimation - Fasheng Wang; Junxing Zhang; Baowei Lin; Xucheng Li (IEEE Access, 2018)
The past several decades have witnessed the successful application of sequential Monte Carlo method (or particle filter) to a variety of fields. It has grown to be a popular method in solving different kinds of nonlinear Bayesian estimation problems. This paper introduces a two-stage particle filter for nonlinear filtering problem. In the proposed particle filter, each particle will be propagated and updated through two stages. At time step t, the first stage refers to using the unscented Kalman filtering equations to propagate the particles from time step t - 1 in order to obtain the preliminary estimations. Then, at the second stage, the particles will be updated again by the iterated extended Kalman filter to yield the final updated particles. In this way, the estimation accuracy of particle filter can be improved, which is validated through simulation experiments and real-world application experiments.