摘要
为推动实时高精度定位技术的发展,文章通过构建历元差分模型,实现了对全球定位系统(Global Positioning System,GPS)与北斗卫星导航系统(BeiDou Navigation Satellite System,BDS)精密卫星钟差的融合估计,并选取全球均匀分布的45个地面跟踪站进行仿实时钟差解算实验。结果表明:自估GPS和BDS钟差与国际全球卫星导航系统服务(International GNSS Service,IGS)事后精密钟差产品的二次差标准差分别优于0.2、0.4 ns,平均精度为0.11、0.19 ns;基于自估钟差的GPS静态和动态精密单点定位(precise point positioning,PPP)精度分别优于2、15 cm,BDS静态和动态PPP精度分别优于5、40 cm,均可满足用户dm级定位需求。
In order to promote the development of real-time and high-precision positioning technology,the epoch differenced model was constructed to realize the fusion estimation of precise satellite clock error between Global Positioning System(GPS)and BeiDou Navigation Satellite System(BDS).The simulated real-time clock error calculation experiments were carried out at 45 tracking ground stations with uniform distribution in the world.The results show that the standard deviation of the second-order difference between the self-estimated GPS and BDS clock error products and International GNSS Service(IGS)final clock error product is better than 0.2 ns and 0.4 ns,and the average accuracy is 0.11 ns and 0.19 ns,respectively.The accuracy of GPS static and dynamic precise point positioning(PPP)based on self-estimation clock error is better than 2 cm and 15 cm respectively,and the accuracy of BDS static and dynamic PPP is better than 5 cm and 40 cm respectively,which can meet the user’s decimetre level positioning requirements.
作者
张浩
赵兴旺
陈佩文
谢毅
ZHANG Hao;ZHAO Xingwang;CHEN Peiwen;XIE Yi(School of Spatial Information and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232001, China;Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Anhui University of Science and Technology, Huainan 232001, China;Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan 232001, China)
出处
《合肥工业大学学报（自然科学版）》
CAS
北大核心
2020年第9期1192-1196,共5页
Journal of Hefei University of Technology：Natural Science
基金
国家自然科学基金资助项目(41704008)
安徽理工大学青年教师科学研究基金资助项目(QN201512)
安徽理工大学研究生创新基金资助项目(2019CX2080)。
