Research on improving accuracy of GPS positioning based on particle filter

To solve the error of GPS positioning based on traditional Kalman filter(KF) and the problem of KF in dealing with nonlinear system and non-Gaussian noise of GPS data filter. A filtering algorithm based on particle filter is proposed to improve the positioning accuracy of GPS receiver. The important density function is set up, which is based on the non-Gaussian error distribution of pseudorange observations values. It is combined particle filter with GPS system nonlinear dynamic state-space model. The experimental results show that particle filter algorithm can deal effectively with non-linear and non-Gaussian state estimation. Compared with positioning optimization algorithm based on KF ,the particle filter algorithm reduces the error of both positioning and speed estimation. The RMSE parameter of particle filter is less than RMSE of KF. It is an effective method to nonlinear and non-Gaussian state estimation problems of GPS positioning filtering.