Combined Positioning of High-Speed Train Based on Improved Adaptive IMM Algorithm

WANG Xiaomin; LEI Xiao; ZHANG Yadong

doi:10.11999/JEIT230251

Volume 46 Issue 3

Mar. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(3): 817-825

WANG Xiaomin, LEI Xiao, ZHANG Yadong. Combined Positioning of High-Speed Train Based on Improved Adaptive IMM Algorithm[J]. Journal of Electronics & Information Technology, 2024, 46(3): 817-825. doi: 10.11999/JEIT230251

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WANG Xiaomin, LEI Xiao, ZHANG Yadong. Combined Positioning of High-Speed Train Based on Improved Adaptive IMM Algorithm[J]. Journal of Electronics & Information Technology, 2024, 46(3): 817-825. doi: 10.11999/JEIT230251

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Combined Positioning of High-Speed Train Based on Improved Adaptive IMM Algorithm

doi: 10.11999/JEIT230251

WANG Xiaomin^{1, 2},
LEI Xiao^{1
,
,},
ZHANG Yadong^{1, 2}

1.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
2.
Sichuan Province Train Operation Control Technology Engineering Research Center, Chengdu 611756, China

Funds: The Science and Technology Research and Development Program of China National Railway Group Corporation (P2021G053, N2021T008, N2021G045, N2022G010), Shanghai Aerospace Science and Technology Innovation Fund funded projects (SAST2020-126)

Received Date: 2023-04-11
Accepted Date: 2023-08-21
Rev Recd Date: 2023-08-21

Available Online: 2023-08-24

Publish Date: 2024-03-27

Abstract

Abstract

A high accuracy combined positioning method for high-speed trains based on the Improved Adaptive Interacting Multiple Model (IMM) is proposed for the high-precision positioning problem of trains. Firstly, a combined positioning scheme of four sensors, namely, satellite receiver, wheel speed sensor, speed radar and single-axis gyroscope, is designed according to the train positioning requirements and the characteristics of each sensor. Next, to address the issue that the IMM fusion filtering algorithm has improper fixed parameter settings due to inaccurate a priori information, the Sage-Husa adaptive filtering and the Transition Probability Matrix (TPM) adaptive update set are introduced to become the adaptive IMM algorithm. To solve the lag problem of multi-model switching, the likelihood function value is set as the judgment flag by using the feature that sub-model likelihood function value can quickly respond to the model change trend, and the judgment window is introduced to correct the TPM matrix elements, which effectively improves the model switching speed. Finally, based on the improved adaptive IMM algorithm, the fusion filtering of four sensor positioning information is carried out to realize the high-precision combined positioning of high-speed trains. Simulation results show that the enhanced algorithm improves the positioning accuracy by 1.6%～14.7% compared with other adaptive IMM algorithms, and it can effectively reduce the peak positional error by increasing the switching speed between models, and it also has a better anti-noise performance.

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References(19)

References

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