期刊文献+

单接收机GNSS数据组合硬件延迟的联合求解方法

A Joint Method for Solving Combined DCB of Single Receiver GNSS Data
在线阅读 下载PDF
导出
摘要 利用GNSS观测数据解算TEC的最大误差源是硬件延迟,包括卫星硬件延迟和接收机硬件延迟.在单接收机情况下,由于数据稀疏以及接收到的卫星信号时间不对齐等特点,已有的解算硬件延迟方法的求解结果往往不理想.在应用局域模式拟合方法和SCORE方法求解单接收机数据基础上,利用局域模型拟合法在电离层平静期拟合较准确的优点,提出一种联合改进方法,同时改正了SCORE方法解算过程中约束过强的缺点.通过利用GPStation-6接收机的GPS和BDS实际观测数据进行解算分析,验证了所提方法的有效性与准确性. The largest error sources of deriving TEC from GNSS observational data are Combined Differential Code Biases(CDCB) of instruments, including both satellite's DCB and receiver's DCB.Current existing methods for solving these DCBs are pretty suitable for multi-receiver cases, but under single receiver cases, they are not ideal for sparse ionospheric puncture points and timeunaligned signals among satellites. A joint method based on the local model fitting method and SCORE method is proposed to improve the solution for the single receiver case. The joint method takes advantage of the higher accuracy of local model fitting method during ionosphere quiet period,meanwhile it overcomes the disadvantage of strong restraint of SCORE method. By using of GPS and BDS data received by GP Station-6 receiver, the practical calculation proves the effectiveness and accuracy of the joint method.
作者 侯维君 李义红 徐步云 杨晓云 刘代志 HOU Weijun LI Yihong XU Buyun YANG Xiaoyun LIU Daizhi(Junior Command College, Rocket Force University of Engineering, Xi'an 710025)
出处 《空间科学学报》 CSCD 北大核心 2017年第5期601-607,共7页 Chinese Journal of Space Science
基金 国家自然科学基金项目资助(41374154)
关键词 GNSS TEC 组合硬件延迟 单接收机 联合方法 GNSS TEC CDCB Single receiver Joint method
作者简介 E—mail:houweij@qq.com
  • 相关文献

参考文献9

二级参考文献67

  • 1毛田,万卫星,刘立波.用经验正交函数构造武汉地区电子浓度总含量的经验模式[J].地球物理学报,2005,48(4):751-758. 被引量:24
  • 2袁运斌,欧吉坤.广义三角级数函数电离层延迟模型[J].自然科学进展,2005,15(8):1015-1019. 被引量:18
  • 3李志刚,程宗颐,冯初刚,李伟超,李慧茹.电离层预报模型研究[J].地球物理学报,2007,50(2):327-337. 被引量:65
  • 4Blanch J. Using Kriging to Bound Satellite Ranging Errors Due to the Ionosphere[D]. California.. Stanford University, 2003.
  • 5Chao Y C. Real Time Implementation of the Wide Area Augmentation System for the Global Positioning System with Emphasis on Ionospheric Modeling[D]. California: Stanford University, 1997.
  • 6Hansen A J. Tomographic Estimation of the Ionosphere Using Terrestrial Sensors [D]. California: Stanford University, 2002.
  • 7Sehaer S, Beutler G, Rothacher M, et al. Daily Global Ionosphere Maps Based on GPS Carrier Phase Data Routinely Produced by the CODE[C] IGS 1996 Analysis Center Workshop, Switzerland, 1996.
  • 8Brown R G, Hwang P Y C. Introduction to Random Signals and Applied Kalman Filtering[M]. Toronto: John Willey & Sons, 1997.
  • 9Liu Z Z. Ionosphere Tomography Modeling and Applications Using Global Positioning System(GPS) Measurements [D]. Calgary: University of Calgary, 2003.
  • 10Chui C K, Chen G R. Kalman Filtering with Real- Time Applications [ M]. New York: Springer-Verlag, 2008.

共引文献120

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部