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

Previous Articles     Next Articles

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

[1] KHALEGHI B,KHAMIS A,KARRAY F O,et al.Multisensor data fusion:A review of the state-of-the-art[J].Information Fusion,2013,14(1):28-44.
[2] FENG S,LIJUN T.Initial alignment of large azimuth misalignment angle in SINS based on CKF [J].Chinese Journal of Scientific Instrument,2012,33(2):327-333.
[3] HAO Y L,YANG J W,CHEN L,et al.Initial alignment ofSINS on dynamic base based on NPF-CKF[J].Journal of Chinese Inertial Technology,2011,19(6):654-658.(in Chinese) 郝燕玲,杨峻巍,陈亮,等.基于NPF-CKF的捷联惯导系统动基座初始对准技术[J].中国惯性技术学报,2011,19(6):654-658.
[4] GE Q,LI W,WEN C.SCKF-STF-CN:a universal nonlinear filter for maneuver target tracking[J].Journal of Zhejiang University SCIENCE C,2011,12(8):678-686.
[5] ZHANG Y,RUI G S,MIAO J,et al.Location Technology Based on the Extend Cubature Kalman Filter[J].Opto-Electronic Engineering,2012,39(4):37-43.(in Chinese) 张洋,芮国胜,苗俊,等.扩展容积卡尔曼滤波定位技术研究[J].光电工程,2012.39(4):37-43.
[6] GE Q B,LI W B,SUN R Y,et al.Research on centralized fusion algorithms based on EKF for multisensor non-linear systems[J].Acta Automatica Sinica,2013,39(6):816-825.
[7] CUI P Y,ZHENG L F,PEI F J,et al.Study on integrated navigation method based on self adjusting particle filter[J].Compu-ter Engineering,2008,34(14):185-187.(in Chinese) 崔平远,郑黎方,裴福俊,等.基于自调整粒子滤波的组合导航方法研究[J].计算机工程,2008,34(14):185-187.
[8] QIN Z.Improved particle filter and its application in GPS dy-namic positioning[J].Global Positioning System,2010,35(5):25-28.(in Chinese) 秦臻.改进的粒子滤波及其在GPS动态定位中的应用[J].全球定位系统,2010,35(5):25-28.
[9] FENG C,WANG M,JI Q B,et al.Analysis and comparison of particle filter resampling algorithm [J].Journal of system simulation,2009,21(4):1101-1105.(in Chinese) 冯驰,王萌,汲清波,等.粒子滤波器重采样算法的分析与比较[J].系统仿真学报,2009,21(4):1101-1105.
[10] 朱志宇.粒子滤波算法及其应用[M].北京:科学出版社,2010.
[11] GORDON N J,SALMOND D J,SMITH A F M,et al.Novel approach to nonlinear/non-Gaussian Bayesian state estimation[J].IEE Proceedings F-Radar and Signal Processing,IET,1993,140(2):107-113.
[12] SPALL J C.Estimation via markov chain monte carlo[J].IEEE Control Systems,2003,23(2):34-45.
[13] ANDRIEU C,DJURI ′ P M,DOUCET A,et al.Model selection by MCMC computation[J].Signal Processing,2001,81(1):19-37.
[14] GODSILL S,CLAPP T.Improvement strategies for MonteCarlo particle filters[M]∥Sequential Monte Carlo Methods in Practice.Springer New York,2001:139-158.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!