Application of improved particle filter to integrated train positioning system
Wei Li · Journal of Chinese Inertial Technology · 2009
To overcome the particle degeneration phenomenon of particle filter algorithm, the paper combines a Singular Value Decomposition-Unscented Kalman filter (SVD-UKF) with a particle filter, and uses the SVD-UKF to obtain the importance distribution of the particle filter. Thus, it put forward an improved particle filter algorithm. The algorithm introduces the latest observation information into the state estimation. So the estimation accuracy is better than that of conventional particle filter. And the algorithm is strong in robutness for it inherits the merit of high numerical stability of singular value decomposition. By applying the algorithm to train positioning system, conducting numerical simulation, and comparing it with classical particle filter, it is shown that the filtering algorithm proposed in this paper can improve navigation and positioning precision and has high stability.