Optimization-based particle filter for state and parameter estimation

In recent years,the theory of particle filter has been developed and widely used for state and parameter estimation in nonlinear/non-Gaussian systems.Choosing good importance density is a critical issue in particle filter design.In order to improve the approximation of posterior distribution,this paper provides an optimization-based algorithm(the steepest descent method)to generate the proposal distribution and then sample particles from the distribution.This algorithm is applied in 1-D case,and the simulation results show that the proposed particle filter performs better than the extended Kalman filter(EKF),the standard particle filter(PF),the extended Kalman particle filter(PF-EKF)and the unscented particle filter(UPF)both in efficiency and in estimation precision.