基于通信的列车定位系统安全性研究  

Research on security of train positioning system based on communication

作　　者：吴文艾 杜巧玲[1] 巨子琪 WU Wenai;DU Qiaoling;JU Ziqi(Xi'an Traffic Enginering Institute,Xi'an 710300,China)

机构地区：[1]西安交通工程学院,西安710300

出　　处：《自动化与仪器仪表》2020年第9期24-28,共5页Automation & Instrumentation

基　　金：西安交通工程学院中青年项目:“基于通信的列车定位系统安全性研究”(No.19KY-28)。

摘　　要：针对当前列车定位精度需求,以及单一导航容易导致通信失效的问题,提出一种组合导航定位系统。对此,在分析BDS和INS优缺点的基础上,搭建BDS+INS的列车导航定位架构,然后就BDS+INS导航定位测量的原理进行深入分析,最后针对导航定位中最为重要的波形采集问题,在分析传统卡尔曼滤波的基础上,引入RBF神经网络对新息卡尔曼滤波进行改进。最后,通过实测,验证通过RBF改进后的卡尔曼滤波在列车定位精度方面,其误差无论是在速度还是在位置上,都有明显的优势,进而大大提高了定位的精度和安全性。In view of the current requirements of train positioning accuracy and the problem that single navigation is easy to lead to communication failure,an integrated navigation positioning system is proposed.In this regard,on the basis of analyzing the advantages and disadvantages of BDS and INS,the train navigation and positioning architecture of BDS+INS is built,and then the principle of BDS+INS navigation and positioning measurement is analyzed in depth.Finally,aiming at the most important waveform acquisition problem in navigation and positioning,on the basis of analyzing the traditional Kalman filter,RBF neural network is introduced to improve the new information Kalman filter.Finally,through the actual measurement,it is verified that the Kalman filter improved by RBF has obvious advantages in the accuracy of train positioning,whether in speed or in position,which greatly improves the accuracy and safety of positioning.

关 键 词：新息卡尔曼滤波 BDS+INS导航 RBF神经网络 安全性 定位精度 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]