Improving GPS Positioning Accuracy Based on Particle Filter Algorithm

Abstract

To solve the error of GPS positioning based on traditional Kalman filter (KF) in dealing with nonlinear system and non-Gaussian noise. A novel filtering algorithm based on particle filter is proposed to improve the positioning accuracy of GPS receiver. The important density function by observing pseudorange non-Gaussian error distribution is set up. It is combined particle filter with system nonlinear dynamic state-space model. To solve the degeneracy phenomenon of particle filter (PF), Markov Chain and Monte Carlo (MCMC) method is adapted. The experimental results show that particle filter algorithm can effectively deal with non-linear and non-Gaussian state estimation. Compared with positioning optimization algorithm based on extended Kalman filter (EKF), the particle filter algorithm reduces the error of both positioning and speed estimation, attains higher positioning accuracy. The root mean square error (RMSE) parameter of particle filter is less. It is an effective method for GPS positioning to nonlinear and non-Gaussian state estimation problem. Moreover, the particle filter based on MCMC can work as an aided plan to provide higher accurate position when the quality of GPS signal is poor and useless.