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作 者:李思辉 刘超[1] 张开坤 王向阳[3] 段伟 孙景强 LI Sihui;LIU Chao;ZHANG Kaikun;WANG Xiangyang;DUAN Wei;SUN Jingqiang(School of Spatial Information and Geomatics Engineering,Anhui University of Science and Technology,232001,Huainan,Anhui,PRC;Nanjing Institute of Surveying,Mapping&Geotechnical Investigation,Co.,Ltd.,210019,Nanjing,PRC;Xuzhou Coal Mining Group,221140,Xuzhou,Jiangsu,PRC)
机构地区:[1]安徽理工大学空间信息与测绘工程学院,安徽淮南232001 [2]南京市测绘勘察研究院股份有限公司,南京210019 [3]徐州矿务集团有限公司资产开发管理部,江苏徐州221140
出 处:《江西科学》2024年第1期142-147,共6页Jiangxi Science
摘 要:多路径误差是全球导航卫星系统(Global Navigation Satellite System,GNSS)短基线的主要误差源,考虑到GNSS多系统条件下,经典的恒星日滤波方法(Sidereal Filtering,SF)卫星的重访周期难以获取以及半天球模型(Multipath Hemispherical Map,MHM)需要大量数据支持的问题,引入了时间卷积网络模型(Time Convolutional Network,TCN),充分利用其对海量数据的深度挖掘能力,建立GNSS坐标序列中的多路径误差模型,并进行多路径误差的实时削弱。通过连续15 d的实验数据测试,结果表明,新方法克服了经典SF方法难以适用于多系统GNSS多路径误差的削弱,且仅需要1 d的训练样本,即可实现后续多天的多路径误差的实时削弱,其削弱效果明显优于MHM模型,表现出了良好的泛化能力。Multipath error is the main error source of the short baseline of Global Navigation Satellite System(GNSS).Considering the difficulty to obtain the revisit period of the classical Sidereal Filtering(SF)satellite under the GNSS multi-system condition and the problem that the Multipath Hemispherical Map(MHM)requires a large amount of data support,this paper introduces the Time Convolutional Network(TCN)model to make full use of its deep mining capability of massive data to establish the multipath error model in GNSS coordinate sequences,and to perform multipath the real-time weakening of the errors.Through 15 days of continuous experimental data testing,the results showed that the new method overcame the difficulty of applying the classical SF method to the weakening of multi-system GNSS multi-path errors,and required only one day of training samples to realize the real-time weakening of multi-path errors in subsequent days,and its weakening effect was significantly better than that of MHM model,showing good generalization ability.
关 键 词:全球导航卫星系统 多路径误差 时间卷积网络 恒星日滤波 半天球模型
分 类 号:P227[天文地球—大地测量学与测量工程]
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