Train positioning is the key to ensure the transportation and efficient operation of the railway.Due to the low accuracy and the poor real-time of the train positioning,a train positioning system based on global navig...Train positioning is the key to ensure the transportation and efficient operation of the railway.Due to the low accuracy and the poor real-time of the train positioning,a train positioning system based on global navigation satellite system/inertial measurement unit/odometer(GNSS/IMU/ODO)combination framework and a train integrated positioning method based on grey neural network are put forward.A data updating method based on the established grey prediction model of train positioning is put forward,which uses the accumulation and summary of the grey theory for the rough prediction of the data.The purpose of the method is to reduce the noise of the original data.Moreover,the radial basis function(RBF)neural network is introduced to correct residual sequence of the grey prediction model.Compared with the single model calibration,this method can make full use of the advantages of each model,thus getting a high positioning accuracy in the case of small samples and poor information.Experiments show that the method has good real-time performance and high accuracy,and has certain application value.展开更多
基金Gansu Province Basic Research Innovation Group Plan(No.1606RJIA327)Natural Science Foundation of Gansu Province(No.1606RJYA225)+1 种基金Lanzhou Jiaotong University Youth Fund(No.2014031)Longyuan Youth Innovative Support Program(No.2016-43)
文摘Train positioning is the key to ensure the transportation and efficient operation of the railway.Due to the low accuracy and the poor real-time of the train positioning,a train positioning system based on global navigation satellite system/inertial measurement unit/odometer(GNSS/IMU/ODO)combination framework and a train integrated positioning method based on grey neural network are put forward.A data updating method based on the established grey prediction model of train positioning is put forward,which uses the accumulation and summary of the grey theory for the rough prediction of the data.The purpose of the method is to reduce the noise of the original data.Moreover,the radial basis function(RBF)neural network is introduced to correct residual sequence of the grey prediction model.Compared with the single model calibration,this method can make full use of the advantages of each model,thus getting a high positioning accuracy in the case of small samples and poor information.Experiments show that the method has good real-time performance and high accuracy,and has certain application value.
