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Volume 55 Issue 2
Mar.  2020
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Journal of Southwest Jiaotong University  > 2020  >  55(2): 393-400.
CAI Xuan, WANG Changlin. BeiDou Navigation Satellite System/Inertial Measurement Unit Integrated Train Positioning Method Based on Improved Unscented Kalman Filter Algorithm[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 393-400. doi: 10.3969/j.issn.0258-2724.20170816
Citation: CAI Xuan, WANG Changlin. BeiDou Navigation Satellite System/Inertial Measurement Unit Integrated Train Positioning Method Based on Improved Unscented Kalman Filter Algorithm[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 393-400. doi: 10.3969/j.issn.0258-2724.20170816
shu

BeiDou Navigation Satellite System/Inertial Measurement Unit Integrated Train Positioning Method Based on Improved Unscented Kalman Filter Algorithm

doi: 10.3969/j.issn.0258-2724.20170816
  • Received Date: 17 Nov 2017
  • Rev Recd Date: 26 Apr 2018
    • Available Online: 14 Sep 2018
  • Publish Date: 01 Apr 2020
    Abstract
  • In order to improve the accuracy and continuity of train positioning, BeiDou satellite receiver and inertial measurement unit were employed to construct an on-board integrated positioning system. Given the nonlinearity and robustness in the information fusion estimation of mulit-sensor positioning, an improved unscented Kalman filter (UKF) algorithm was proposed by applying the equivalence of robustness to the standard UKF. With the equivalent transformation of noise covariance in the standard UKF algorithm, the filter gain was adjusted, such that the filtering algorithm has a strong ability to suppress gross errors in sensor observation. The improved UKF algorithm and the standard UKF algorithm were applied to the integrated positioning for simulation comparison. The results show that, the filtering accuracy of the improved UKF is slightly higher than that of the standard UKF under normal conditions; the filtering accuracy and stability of the improved UKF is significantly better than the standard UKF when sensor observations contain gross errors. The average estimation errors of north and east positioning are respectively decreased by 48.5% and 48.8%. The average estimation errors of north and east speed are respectively declined by 43.7% and 48.9%.

     

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    • References(19)
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