王更生,张敏.列车组合定位中改进CPF算法的探讨[J].计算机科学,2017,44(9):296-299
列车组合定位中改进CPF算法的探讨
Research of Improved CPF Algorithm for Intergrated Train Positioning
投稿时间:2016-08-02  修订日期:2017-01-08
DOI:10.11896/j.issn.1002-137X.2017.09.055
中文关键词:  列车组合定位,容积粒子滤波,重要性密度函数,马尔科夫链蒙特卡洛
英文关键词:Integrated train positioning,Cubature particle filter, Importance density function,Markov chain Monte Carlo
基金项目:本文受国家自然科学基金(61461019)资助
作者单位
王更生 华东交通大学信息工程学院 南昌330013 
张敏 华东交通大学信息工程学院 南昌330013 
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中文摘要:
      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。
英文摘要:
      In order to solve the problem that the extended Kalman filter (EKF) and unscented Kalman filter (UKF),which are widely used in the GNSS / INS integrated train positioning,can not meet the complex environment problem of high speed train positioning,a new method based on improved cubature particle filter (CPF) algorithm was proposed for the information fusion of intergrated train positioning.The Markov chain Monte Carlo (MCMC) method was used to solve the particle degeneracy problem,improving the filter performance.Using Matlab simulation,the results show that the improved CPF algorithm has smaller position error and velocity error,whitch improves the accuracy in the process of train nonlinear motion.
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