Recursive Bayes Model Particle Filter Method

Aimed at the issue of the particles degeneracy,the loss of the diversity among the particles and a heavy computational burden in particle filter algorithm,this paper has proposed a particle filter method based on recursive bayes model(Recursive-Bayes-PF). The basic idea is to utilize the system state function and the transition relationship of the probability density of stochastic variable,making that the prediction of probability density function of the state is transferred to the posterior probability density function with an efficient recursive form. Besides,the particles for next iterative course are drawn again according to the current state estimation,so that the new particles distribute in the neighborhood area of the true state as much as possible,increasing the utilizing efficiency of the particles and improving the filtering accuracy.Theoretical analysis and simulation results show that comparing with the classical particle filter and the other various resampling approaches,the proposed method has a much better filtering accuracy but with lower computational cost.