Improved Divided Difference Particle Filter Based on Observation Interation

Based on the concept of sequential importance sampling (SIS) and the use of Bayesian theory,particle filter is particularly useful in dealing with nonlinear and non-Gaussian problems.How to select an important density function to reduce the affection of the particle degeneration and improve the accuracy of the particle filter is one of the major problems.Taking guassian distribution as the proposal distribution,traditional particle filter do not integrate the latest meansurements,so it needs large quatity of particles to match the posterticle densty.In this paper,a new particle filter is proposed that uses a iterated divided difference filter to gengerate the importance proposal distribution is proposed to decrease the posterior probability distribution estimation error,ehhance tracking effect.The proposal distribution integrates the latest meansurements into system state transition density so it can match the posterior densty well.The simulation results show that the new particle filter performs superior to other particle filter.