基于改进型卡尔曼滤波的山地轨道垂向不平顺反演方法研究

丁军君; 冯大年; 甘霖; 翁浩翔; 赵博新

doi:10.19713/j.cnki.43-1423/u.T20251376

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基于改进型卡尔曼滤波的山地轨道垂向不平顺反演方法研究

轨道与基础 | 更新时间：2026-07-24

- 基于改进型卡尔曼滤波的山地轨道垂向不平顺反演方法研究
- Inversion method of mountain track vertical irregularity based on improved Kalman filtering
- 铁道科学与工程学报 2026年23卷第7期页码：3227-3238
- 作者机构：
  
  1.西南交通大学机械工程学院，四川成都 610030
  2.广东松山职业技术学院先进制造学院，广东韶关 512126
- 作者简介：
  
  丁军君(1985—)，男，贵州修文人，副教授，博士，从事机车车辆系统动力学研究；E-mail：dingjunjun@swjtu.edu.cn
- 基金信息：
  
  四川省重大科技专项(23ZDZX0005);2024年度韶关市社会发展科技协同创新体系建设项目(240903108039047);2024年广东松山职业技术学院校级科研项目(2024KJYB08)
- DOI：10.19713/j.cnki.43-1423/u.T20251376
  中图分类号： U216.3
- 收稿：2025-09-02，
  
  网络首发：2026-07-24，
  
  纸质出版：2026-07-28
- 稿件说明：
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丁军君,冯大年,甘霖等.基于改进型卡尔曼滤波的山地轨道垂向不平顺反演方法研究[J].铁道科学与工程学报,2026,23(07):3227-3238.

DING Junjun,FENG Danian,GAN Lin,et al.Inversion method of mountain track vertical irregularity based on improved Kalman filtering[J].Journal of Railway Science and Engineering,2026,23(07):3227-3238.
丁军君,冯大年,甘霖等.基于改进型卡尔曼滤波的山地轨道垂向不平顺反演方法研究[J].铁道科学与工程学报,2026,23(07):3227-3238. DOI： 10.19713/j.cnki.43-1423/u.T20251376.

DING Junjun,FENG Danian,GAN Lin,et al.Inversion method of mountain track vertical irregularity based on improved Kalman filtering[J].Journal of Railway Science and Engineering,2026,23(07):3227-3238. DOI： 10.19713/j.cnki.43-1423/u.T20251376.

摘要

为了研究胶轮/钢轮复合转向架产生垂向振动的主要原因，开展了钢轨/增力轨垂向不平顺反演的相关研究。基于卡尔曼滤波算法建立轨道垂向不平顺反演模型，并通过Sage-Husa自适应滤波算法和遗忘因子对传统的卡尔曼滤波法进行改进，提高其滤波估计值的精度和鲁棒性。建立了21自由度的胶轮/钢轮复合转向架车辆垂向动力学模型，选取车体、构架的绝对加速度和相对位移、角速度等状态量作为观测量组合，分别对美国4级谱下的钢轨垂向不平顺和公路A级谱下的增力轨垂向不平顺进行反演，并通过改进前后反演结果的相关误差对2种方法的准确性进行比较。研究结果表明：改进型卡尔曼滤波算法能够准确地对钢轨和增力轨垂向不平顺进行反演，反演结果与真实值之间的皮尔逊相关系数均大于0.9。在直线线路上，改进型卡尔曼滤波算法的最大误差、平均误差和误差均方根值与传统卡尔曼滤波算法相比均降低了90%以上；在曲线线路上，改进型卡尔曼滤波算法的反演效果相比于传统卡尔曼滤波算法仍有显著提升。随着运行速度的变化，直线和曲线线路轨道不平顺的反演结果与实际值的相关系数均大于0.9，呈现高度相关性，算法鲁棒性较好。研究结果可为进一步提高轨道不平顺反演精度、保障山地轨道车辆安全及养护决策提供参考。

Abstract

To investigate the main causes of vertical vibration generated by the rubber/steel wheel composite bogie

relevant research on the inversion of vertical irregularities of rails and enhanced rails was carried out. Based on the Kalman filtering algorithm

a track vertical irregularity inversion model was established

and the traditional Kalman filtering method was improved by using the Sage-Husa adaptive filtering algorithm and forgetting factor to enhance its filtering estimation accuracy and robustness. A 21-degree-of-freedom rubber/steel wheel composite bogie vehicle vertical dynamics model was established. The absolute acceleration and relative displacement

angular velocity and other state quantities of the vehicle body and frame were selected as measurement combinations. The vertical irregularities of rails under the American 4th grade spectrum and enhanced rails under the highway Ath grade spectrum were inverted respectively

and the correlation errors before and after the improvement were used to compare the accuracy of the two methods. The research results show that the improved Kalman filtering algorithm can accurately invert the vertical irregularities of rails and enhanced rails. The Pearson correlation coefficient between the inversion results and the true values is greater than 0.9. On straight tracks

the maximum error

average error and root mean square error of the improved Kalman filtering algorithm are reduced by more than 90% compared with the traditional Kalman filtering algorithm

and the inversion effect of the improved Kalman filtering algorithm on curved tracks is still significantly improved compared with the traditional Kalman filtering algorithm. With the change of running speed

the correlation coefficients of the inversion results of track irregularities on straight and curved tracks with the actual values are greater than 0.9

showing a high correlation

and the algorithm has good robustness. The research results can provide a reference for further optimizing the accuracy of track irregularity inversion

ensuring the safety of mountain railway vehicles and making maintenance decisions.

关键词

Keywords

references

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