摘要
为实时掌握列车运行多因子环境,研究适用于高速铁路运行场景监测的数据融合算法,并针对现有多传感器一致性数据融合方法存在的缺陷,提出一种在线迭代与局部融合结合的数据融合算法,以一种新的置信距离和自适应加权估计算法为理论基础,克服了传统支持度指标非0即1的主观性和绝对化,增加了精度较低的传感器参与数据融合的可能性.实验结果表明,提出的算法有效的提高了监测信息的准确性和可靠度,可应用于测量数据庞大的铁路运行环境数据级融合过程.
In order to master the multi-factor environment of train operation in real time,the data fusion algorithm suitable for high-speed railway operation scene monitoring is studied,based on a new confidence distance and adaptive weighting estimation algorithm,a new data fusion algorithm combining on-line iteration and local fusion is proposed,which overcomes the subjectivity and absoluteness of traditional support index which is either 0 or 1,it increases the possibility of the sensor with low precision to participate in data fusion.Experimental results show that the proposed algorithm effectively improves the monitoring sourcing accuracy and reliability.It can be applied to the data level fusion process in the railway operation environment with huge measurement data.
作者
胥如迅
马军惠
孟建军
李德仓
陈晓强
XU Ru-xun;MA Jun-hui;MENG Jian-jun;LI De-cang;CHEN Xiao-qiang(Mechatronics T&R Institute,Lanzhou Jiaotong University,Lanzhou 730070,China;Gansu Provincial Engineering Technology Center for Informatization of Logistics&Transport Equipment,Lanzhou 730070,China;Gansu Provincial Industry Technology Center of Logistics&Transport Equipment,Lanzhou 730070,China;School of mechanical and electrical engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)
出处
《兰州交通大学学报》
CAS
2022年第4期76-81,共6页
Journal of Lanzhou Jiaotong University
基金
国家自然科学基金项目(72061021、62063013)
甘肃省科技计划项目(20JR10RA251)
兰州交通大学校青年基金项目(2021018)资助。
关键词
铁路运行
数据融合
自适应加权融合
置信距离
在线迭代
railway operation
data fusion
adaptive weighted fusion
confidence distance
online iteration
