Computer Science ›› 2017, Vol. 44 ›› Issue (9): 296-299.doi: 10.11896/j.issn.1002-137X.2017.09.055

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Research of Improved CPF Algorithm for Intergrated Train Positioning

WANG Geng-sheng and ZHANG Min   

  • Online:2018-11-13 Published:2018-11-13

Abstract: 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.

Key words: Integrated train positioning,Cubature particle filter, Importance density function,Markov chain Monte Carlo

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