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基于改进自适应IMM算法的高速列车组合定位

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针对列车高精度定位问题,该文提出基于改进自适应交互多模型(IMM)的高速列车高精度组合定位方法.首先,根据列车定位需求和各传感器特点,设计了卫星接收器、轮轴测速传感器、测速雷达以及单轴陀螺仪4种传感器的组合定位方案.然后,针对IMM融合滤波算法因先验信息不准导致固定参数设置不当的问题,引入Sage-Husa自适应滤波和转移概率矩阵(TPM)自适应更新集成为自适应IMM算法.针对多模型切换的滞后问题,利用子模型似然函数值能快速反映模型变化趋势的特点,将似然函数值设为判定标志,并引入判定窗对TPM矩阵元素进行修正,有效提升了模型的切换速度.最后,基于改进自适应IMM算法对4种传感器定位信息进行融合滤波,实现高速列车的高精度组合定位.仿真结果表明:改进后的算法相比其他自适应IMM算法提升定位精度1.6％～14.7％,并且能通过提高模型间切换速度来有效降低位置误差峰值,同时具备较好的抗噪性能.
Combined Positioning of High-Speed Train Based on Improved Adaptive IMM Algorithm
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.

Train positioningImproved Adaptive Interacting Multiple Model (IMM)Sage-Husa adaptive filterMarkov Transition Probability Matrix (TPM)Judgement window

王小敏、雷筱、张亚东

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西南交通大学信息科学与技术学院 成都 611756

四川省列车运行控制技术工程研究中心 成都 611756

列车定位 交互式多模型 Sage-Husa自适应滤波算法 马尔可夫转移概率矩阵 判定窗

中国国家铁路集团有限公司科技研发计划中国国家铁路集团有限公司科技研发计划中国国家铁路集团有限公司科技研发计划中国国家铁路集团有限公司科技研发计划上海航天科技创新基金(SAST基金)

P2021G053N2021T008N2021G045N2022G010SAST2020-126

2024

10.11999/JEIT230251

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子：1.302
ISSN：1009-5896
年,卷(期)：2024.46(3)
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