Particle Filter Target Tracking Algorithm Based on MCMC Iteration Cubature

Abstract

In view of the problem of particle degradation and tracking accuracy in the standard particle filter tracking target algorithm, a new improved particle filter algorithm called Iterated Cubature Kalman Particle Filter (ICKPF) is proposed in this paper.The new ICKPF algorithm is based on the Markov Chain Monte Carlo (MCMC), and the cubature rule based on numerical integration method is used to calculate the mean and covariance, which generates the proposal distribution for the particle filter.The current measurements are integrated into the proposal distribution.Therefore, degree of approximation to the system posterior density is improved.Simulation results show that the estimation error of the ICKPF-MCMC algorithm is much better than other algorithms.