列车组合定位中改进CPF算法的探讨
发布时间：2019-02-18 09:54
【摘要】：针对在GNSS/INS列车组合定位中普遍采用的扩展卡尔曼滤波(EKF)、无迹卡尔曼滤波(UKF)等滤波技术无法满足复杂的高速列车组合定位环境问题,研究了列车组合定位中改进的容积粒子滤波(CPF)算法,提出了基于改进CPF算法的列车组合定位信息融合技术。该算法采用马尔科夫链蒙特卡洛(MCMC)移动方法来解决粒子退化问题,进而提高滤波性能。使用Matlab对改进算法进行仿真,结果表明改进CPF具有更小的位置误差和速度误差,提高了列车非线性运动过程中的定位精度。
[Abstract]:The extended Kalman filter (EKF),) and unscented Kalman filter (UKF), which are widely used in GNSS/INS train combination positioning, can not meet the complex environment problems of high-speed train combination positioning. The improved volumetric particle filter (CPF) algorithm in train combined positioning is studied, and the information fusion technology based on improved CPF algorithm is proposed. The Markov chain Monte Carlo (MCMC) method is used to solve the particle degradation problem and the filtering performance is improved. Matlab is used to simulate the improved algorithm. The results show that the improved CPF has smaller position error and speed error, and improves the positioning accuracy in the process of nonlinear train motion.
【作者单位】： 华东交通大学信息工程学院;
【基金】：国家自然科学基金(61461019)资助
【分类号】：TN713
本文编号：2425739
[Abstract]:The extended Kalman filter (EKF),) and unscented Kalman filter (UKF), which are widely used in GNSS/INS train combination positioning, can not meet the complex environment problems of high-speed train combination positioning. The improved volumetric particle filter (CPF) algorithm in train combined positioning is studied, and the information fusion technology based on improved CPF algorithm is proposed. The Markov chain Monte Carlo (MCMC) method is used to solve the particle degradation problem and the filtering performance is improved. Matlab is used to simulate the improved algorithm. The results show that the improved CPF has smaller position error and speed error, and improves the positioning accuracy in the process of nonlinear train motion.
【作者单位】： 华东交通大学信息工程学院;
【基金】：国家自然科学基金(61461019)资助
【分类号】：TN713
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