Application simulation research of Gaussian particle filtering in train integrated position system
Baigen Cai, Yi An, Shang Guan-wei, Jiang Liu, Jian Wang · 2011
Data fusion algorithm is an important guarantee about the performance level and complete function of the train positioning system. Since conventional Kalman filter methods in GNSS/INS integration frame could not solve the problem of nonlinear model. In this paper the Gaussian particle filter (GPF) is introduced, which is an efficient variant on the particle filtering algorithm for nonlinear hybrid systems. Simulation shows that the filtering precision can meet the navigation system's requirements. Due to the relaxed restriction of the system model and non-Gaussian noise, GPF has advantages in a direct filter system compared with other methods.