基于GPS与IMU组合传感器数据融合的有轨电车定位分析  

Tram Positioning Analysis Based on GPSand IMU Combined Sensor Data Fusion

作　　者：韩如坤[1] HAN Ru-kun(Yantai Automotive Engineering College Department of Information and Control Engineering,Yantai 265500 China)

机构地区：[1]烟台汽车工程职业学院信息与控制工程系

出　　处：《自动化技术与应用》2019年第8期65-68,共4页Techniques of Automation and Applications

摘　　要：给出了一种建立在模糊投票方式基础上的GPS和IMU相结合的定位方式,充分消除了采用单一传感器无法进行精确定位的问题。总共使用3个IMU来完成模糊投票决策,并结合模糊分析准则,对各输入量置信度进行判断,并得到相应的置信度集合。并通过具备自适应能力的联邦Kalman滤波器来融合IMU与GPS的数据,完成可容错分析。利用仿真软件测试了GPS故障诊断隔离单元的运行状态,同时把故障偏差添加到伪距内,将上述结果和单独GPS进行了比较。该系统的故障识别率相对于GPS已经获得显著提升。A GPS and IMU positioning method based on fuzzy voting method is presented,which fully eliminates the problem that single sensor cannot be used for accurate positioning.A total of 3 IMU are used to complete the fuzzy voting decision,and combined with the fuzzy analysis criteria,the confidence of each input is judged,and the corresponding confidence set is obtained.Moreover,the data of IMU and GPS were integrated through the federal Kalman filter with adaptive ability to complete the fault-tolerant analysis.The running state of GPS fault diagnosis isolation unit is tested by simulation software,and the fault deviation is added into pseudo-distance.The fault identification rate of the system has been significantly improved relative to GPS.

关 键 词：GPS 组合导航系统 现代有轨电车 模糊投票机制 卡尔曼滤波 

分 类 号：U482.1[交通运输工程—载运工具运用工程]