期刊文献+

采样间隔和VTEC模型对GPS硬件延迟估计的影响分析 被引量:1

ANALYSIS OF INFLUENCE OF SAMPLING INTERVAL AND VTEC MODEL ON ESTIMATION OF GPS INSTRUMENTAL BIASES
下载PDF
导出
摘要 通过实测数据分析了采样间隔和VTEC拟合模型对解算卫星和接收机硬件延迟的影响。计算结果表明,对于连续运行的GPS观测站,采用3小时采样间隔和立方模型,可以正确解算出硬件延迟和有效减小计算量。 In the calculation of ionosphere distribution,it is a key to eliminate the instrumental biases of satellite and receiver from GPS dual-frequency observations.The effects of changing sampling interval and VTEC fitting model were analyzed.The calculation results indicate that the 3-hour sampling interval and cubic fitting model are sufficient for computing the instrumental biases on continuous GPS stations.
出处 《大地测量与地球动力学》 CSCD 北大核心 2010年第6期112-115,共4页 Journal of Geodesy and Geodynamics
关键词 电离层 硬件延迟 采样间隔 垂向电子总含量 立方模型 ionosphere instrumental biases sampling interval VTEC cubic fitting model
  • 相关文献

参考文献6

  • 1Wilson B D and Mannicci A J.Instrumental biases in ionosphere measurements derived from GPS data[C].Proceedings of ION GPS-93,Salt Lake City,1993.
  • 2Coco D S,et al.Variability of GPS satellite differential group delay biases[J].IEEE transaction on aerospace and electrical systems,1991,27(6):931-938.
  • 3Ma G and Maruyama T.Derivation of TEC and estimation of instrumental biases from GEONET in Japan[J].Annales Geophysicae,2003,21:2 083-2 093.
  • 4Mannucci A J,Wilson B D and Edwards C D.A new method for monitoring the earth ionospheric total electron content using the GPS global network[C].Proceedings of ION GPS-93,Salt Lake City,1993.
  • 5Lin Laosheng.Remote sensing of ionosphere using GPS measurements[C].The 22nd Asian Conference on Remote Sensing,Singapore,2001.
  • 6Klobuchar J A,Basu S and Doherty P.Potential limitations in making absolute ionospheric measurements using dual frequency radio waves from GPS satellites[C].Proceedings of Ionospheric Effects Symposium,Alexandria,1993.

同被引文献20

  • 1罗党,刘思峰.灰色关联决策方法研究[J].中国管理科学,2005,13(1):101-106. 被引量:162
  • 2李孟良,张富兴,李宏光,艾国和.不同采样间隔对车辆行驶工况测定影响的研究[J].汽车工程,2005,27(3):316-318. 被引量:5
  • 3Schmiegel A U,Kleine A.Optimized operation strategies for PV storages systems yield limitations,optimized battery configuration and the benefit of a perfect forecast[J].Energy Procedia,2014,46:104-113.
  • 4Darras C,Muselli M,Poggi P,et al.PV output power fluctuations smoothing:The MYRTE platform experience[J].International Journal of Hydrogen Energy,2012,37(19):14015-14025.
  • 5Hudson R,Heilscher G.PV grid integration-system management issues and utility concerns[J].Energy Procedia,2012,25:82-92.
  • 6Mellit A,Pavan A M.A 24-h forecast of solar irradiance using artificial neural network:Application for performance prediction of a grid-connected PV plant at Trieste,Italy[J].Solar Energy,2010,84(5):807-821.
  • 7Coughlin K,Murthi A,Eto J.Multi-scale analysis of wind power and load time series data[J].Renewable Energy,2014,68:494-504.
  • 8Pawlus P,Zelasko W.The importance of sampling interval for rough contact mechanics[J].Wear,2012,276-277:121-129.
  • 9Kayacan E,Ulutas B,Kaynak O.Grey system theory-based models in time series prediction[J].Expert Systems with Applications,2010,37(2):1784-1789.
  • 10Golmohammadi D,Mellat-Parast M.Developing a grey-based decision-making model for supplier selection [J].International Journal of Production Economics,2012,137(2):191-200.

引证文献1

二级引证文献4

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