摘要
针对北斗卫星导航系统(BeiDou Navigation Satellite System,BDS)精密单点定位(precise point positioning,PPP)服务PPP-B2b在同一时刻可用于PPP的卫星数量有限,以及其实时钟差解算评估研究较少的问题,本文提出了一种基于BDS PPP-B2b与Galileo高精度服务(high accuracy service,HAS)融合的改进方案,并对其实时钟差解算性能进行了系统性评估.该方法利用两个卫星系统的PPP服务改正数进行融合,显著提升了同一时刻内可用卫星数量.相较于单一PPP-B2b服务方案,在钟差解算的精度和稳定性方面具有明显提升.同时,实验结果表明,该方法在经过一定时间的收敛后,其性能优于德国波兹坦地学研究中心(German Research Centre for Geosciences,GFZ)提供的超快速(UTL)产品.因此,本文不仅为提高PPP-B2b服务的实际应用能力提供了有效途径,也为多系统融合下的实时时间解算方法设计与性能优化提供了新的思路和实验验证.
In response to the limited number of satellites available for precise point positioning(PPP)at the same time with BeiDou Navigation Satellite System(BDS)PPP-B2b and the lack of research on real-time clock offset estimation,an improved method based on the fusion of BDS PPP-B2b and Galileo high accuracy service(HAS)is proposed,and its real-time clock bias estimation performance is systematically evaluated.The method fuses the PPP state space representation(SSR)corrections from both satellite systems,significantly increasing the number of satellites available at the same epoch.Compared to the standalone PPPB2b service,this approach demonstrates substantial improvements in the accuracy and stability of clock bias estimation.Experimental results show that,after a certain period of convergence,the method outperforms the ultra-fast(UTL)product provided by GFZ.Therefore,this work not only provides an effective way to enhance the practical application of PPP-B2b services but also offers new insights and experimental validation for the design and performance optimization of real-time time solutions in multi-system integration.
作者
欧文浩
刘筱琪
欧阳明俊
刘阳
朱祥维
OU Wenhao;LIU Xiaoqi;OUYANG Mingjun;LIU Yang;ZHU Xiangwei(School of Electronics and Communication Engineering,Sun Yat-sen University,Shenzhen 518107,China;College of Electronics and Information Technology,Guangdong Polytechnic Normal University,Guangzhou 510665,China;Guangdong TOPS SOFT-PARK Co.,Ltd.,Guangzhou 510663,China)
出处
《全球定位系统》
2025年第3期3-7,30,共6页
Gnss World of China
基金
国家重点研发计划(2021YFA0716500)
国家自然科学基金(61973328,91938301)
深圳市基础研究重点项目(2020N259)。
