基于电磁感应动态模型的欧标应答器系统定位研究

Research on the Positioning of Eurobalise System Based on Electromagnetic Induction Dynamic Model

采用欧标应答器系统获得的列车绝对位置存在不确定性,不确定性的增加会降低列车的停车精度。通过分析欧标应答器位置修正系统的构成及内部信息流,发现欧标应答器系统有效电磁作用时间误差和速度测量误差对其获得的绝对位置不确定性有较大影响。针对有效电磁作用时间问题,采用四边形模拟应答器发送天线、八边形模拟应答器传输模块接收天线建立应答器系统的磁通量分布模型,并引入磁通量随列车运动的变化,得到磁场感应电动势的动态速度模型(电磁感应动态模型)。基于该模型分析动生电动势、感生电动势与电磁作用距离的关系及干扰、旁瓣对电磁作用距离的影响;通过分析现场实测列车定位数据的电磁感应有效区域数值分布,验证电磁感应动态模型计算数据与实测数据的相符性。结果表明:支持向量机SVM从基于电磁感应动态模型标记的现场实测训练数据中学习得到的应答器位置修正异常分类模型可以实时对列车应答器位置修正做出评估,对于偶然性干扰或旁瓣等导致的异常修正可以给出报警;基于电磁感应动态模型对实测电磁作用时间数据进行修正后,可降低时间数据带来的相对平均误差达47.7%,从而获得更为精确的列车绝对位置,提高列车停车精度。

It has been found that uncertainty occurs in train's absolute position obtained by the Eurobalise system,and the increase of uncertainty will reduce the parking accuracy of the train.By analyzing the composition and the internal information flow of the Eurobalise position correction system,it is found that the error of effective electromagnetic action time of the Eurobalise system and the velocity measurement error have a great impact on the absolute position uncertainty obtained from the system.For the issue of effective electromagnetic action time,the magnetic flux distribution model of the balise system is established by using quadrilateral analog balise to transmit antenna and octagonal analog balise transmission module to receive antenna;and the dynamic velocity model of magnetic field induced electrodynamic force(electromagnetic induction dynamic model)is obtained by introducing the change of magnetic flux along with the train movement.Based on this model,the relationship between the motional electromotive force,induced electromotive force and the distance of electromagnetic action,and the influences of interference and sidelobe on the distance of electromagnetic action are analyzed.Consistency between the calculated data of the electromagnetic induction dynamic model and the field measured data is verified by analyzing the numerical distribution of electromagnetic induction effective area of the field measured train positioning data.The results show that the abnormal classification model of balise position correction learned by support vector machine SVM based on the field measured training data marked by the electromagnetic induction dynamic model can conduct real-time evaluation for the balise position correction of the train and raise the alarm for abnormal correction caused by accidental interference or sidelobe.The relative average error caused by time data can be reduced to 47.7%since the measured data of magnetic action time has been corrected based on electromagnetic induction dynamic model.Hence,the proposed model can obtain a more accurate absolute position of the train and improve the parking accuracy of the train.