典型文献
Non-Line-of-Sight Multipath Detection Method for BDS/GPS Fusion System Based on Deep Learning
文献摘要：
Non-line-of-sight(NLOS)multipath effect is the main factor that restricts the application of global navigation satellite system(GNSS)in complex environments,especially in urban canyon.The effective avoidance of NLOS signals can significantly improve the positioning performance of GNSS receiver.In this paper,an NLOS/LOS classification model based on recurrent neural network is proposed to classify satellite signals received in urban canyon environments.The accuracy of classification is 91％,and the recognition rate of NLOS is 89％;the classification performance is better than that of traditional machine learning classification models such as support vector machine.For BeiDou navigation satellite system/global positioning system(BDS/GPS)fusion system,the least square algorithm and extended Kalman filter are used to estimate the position.The experimental results show that the three-dimensional positioning accuracy after NLOS recognition is improved about 60％on average compared with the traditional methods,and the positioning stability is also improved significantly.
文献关键词：
中图分类号：
作者姓名：
SU Hong;WU Bozhao;MAO Xuchu
作者机构：
School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;Xi'an Satellite Control Center,Xi'an 710043,China
文献出处：
引用格式：
[1]SU Hong;WU Bozhao;MAO Xuchu-.Non-Line-of-Sight Multipath Detection Method for BDS/GPS Fusion System Based on Deep Learning)[J].上海交通大学学报（英文版）,2022(06):844-854
A类：
B类：
Non,Line,Sight,Multipath,Detection,Method,BDS,GPS,Fusion,System,Based,Deep,Learning,line,sight,NLOS,multipath,main,that,restricts,application,global,navigation,satellite,system,GNSS,complex,environments,especially,urban,canyon,effective,avoidance,signals,significantly,positioning,performance,receiver,In,this,paper,classification,recurrent,neural,network,proposed,classify,received,accuracy,recognition,rate,better,than,traditional,machine,learning,models,such,support,vector,For,BeiDou,fusion,least,square,algorithm,extended,Kalman,filter,used,estimate,experimental,results,show,three,dimensional,after,improved,about,average,compared,methods,stability,also
AB值：
0.556328
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