The iterated algorithm used in nonlinear Kalman Filter fusion

The idea of particle filter is to represent probability density function of nonlinear system by a set of random samples. In order to overcome the flaw that it is hard to get the optimization importance density function in the particle filter, in this paper, the iterated algorithm is used in the Extended Kalman Filter (EKF) and the sequential fusion integrated with particle filter. Besides, the iterated algorithm is used in the Unscented Kalman Filter (UKF) to generate the proposal distribution for particle filter. To evaluate the efficacy of the iterated algorithm, we apply it in a real-world estimation problem, the simulation results are compared against those of widely used Nonlinear Kalman Filter fusion.