A Track Occupancy Identification Approach Based on Bayesian Modeling

Journal of Transportation Systems Engineering and Information Technology ›› 2020, Vol. 20 ›› Issue (1): 47-53.

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A Track Occupancy Identification Approach Based on Bayesian Modeling

GUO Zi-ming^a, CAI Bai-gen^a,b,c, JIANGWei^a,b,c,WANG Jian^a,b,c, SHANGGUANWei^a,b,c

a. School of Electronic and Information Engineering; b. State Key Laboratory of Rail Traffic Control and Safety; c. Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation, Beijing Jiaotong University, Beijing 100044, China

Received:2019-10-28 Revised:2019-12-12 Online:2020-02-25 Published:2020-03-02

基于贝叶斯建模的轨道占用识别方法

郭子明^a，蔡伯根^a,b,c，姜维^*a,b,c，王剑^a,b,c，上官伟^a,b,c

北京交通大学a. 电子信息工程学院；b. 轨道交通控制与安全国家重点实验室； c. 北京市轨道交通电磁兼容与卫星导航工程技术研究中心，北京 100044

作者简介:郭子明(1992-)，男，山东威海人，博士生.
基金资助:
国家重点研发计划/National Key Research and Development Program of China (2018YFB1201500)；国家自然科学基金/National Natural Science Foundation of China (61703034)；北京市自然科学基金/Natural Science Foundation of Beijing, China(4184096).

Abstract

Abstract:

The identification of the present track is an essential applications of train control systems. This paper presents a track occupancy identification approach based on Bayesian modeling with the aid of a digital track map. Firstly, map matching is performed considering the direction-dependent GNSS standard deviation while velocity measurements are processed using Kalman filtering. The longitudinal train position is obtained by fusing GNSS and velocity information by means of weighted averaging. Then a Bayesian model is built for hypotheses of the train position and the probabilities of all possible hypotheses given specific GNSS and velocity measurements are calculated. Finally, the probabilities are compared with a defined threshold. Moreover, the identification results are classified. The experimental results show that the track occupancy identification approach based on Bayesian modeling can reduce the distance to eliminate hypotheses with negligible probabilities. Compared to an orthogonal projection algorithm, this approach is more confident in the determination of the track that the train takes.

Key words: railway transportation, track occupancy identification, Bayesian modeling, sensor fusion, map matching

摘要：

识别列车所在轨道，是列车运行控制系统必不可少的功能. 提出一种数字轨道地图辅助的基于贝叶斯建模的轨道占用识别方法. 首先，在考虑全球导航卫星系统(Global Navigation Satellite System, GNSS)与方向相关测量误差的基础上进行地图匹配，采用卡尔曼滤波算法处理速度测量值，通过加权求和的方式对GNSS与速度信息进行融合，获得列车沿轨道方向的一维位置；其次，对列车位置假设进行贝叶斯建模，计算在给定GNSS与速度测量的前提下所有可能的位置假设的概率；最后，与设置的假设概率阈值进行比较，对不同的识别结果进行分类. 实验结果表明，基于贝叶斯建模的轨道占用识别法能够减少剔除小概率假设所需的距离，与垂直投影法相比，该方法可以对列车所在轨道做出更确定的判断.

关键词: 铁路运输, 轨道占用识别, 贝叶斯建模, 传感器融合, 地图匹配

CLC Number:

U284

GUO Zi-ming, CAI Bai-gen, JIANGWei,WANG Jian, SHANGGUANWei. A Track Occupancy Identification Approach Based on Bayesian Modeling[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(1): 47-53.

郭子明，蔡伯根，姜维，王剑，上官伟. 基于贝叶斯建模的轨道占用识别方法[J]. 交通运输系统工程与信息, 2020, 20(1): 47-53.

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A Track Occupancy Identification Approach Based on Bayesian Modeling

基于贝叶斯建模的轨道占用识别方法

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