Monte Carlo Markov Chain Cubature Particle Filter

A novel improved particle filter based on sequential importance sampling, Monte Carlo Markov Chain (MCMC) cubature particle filter, is proposed for the estimation of non-linear non-Gaussian system. Each particle is estimated by means of cubature Kalman filter. The importance density function gets closer to the real posterior after taking the current observation into consideration on the basis of state transition. MCMC step is added after the selection. The theoretical analysis and the simulation experiment show the cubature particle filter performs much better than the other parallel filters.