测绘通报 ›› 2020, Vol. 0 ›› Issue (8): 76-80.doi: 10.13474/j.cnki.11-2246.2020.0252

• 学术研究 • 上一篇    下一篇

大范围GNSS水准与重力场模型间的系统偏差校正

陈鑑华, 魏德宏, 张兴福, 李伟超   

  1. 广东工业大学测绘工程系, 广东 广州 510006
  • 收稿日期:2020-04-24 修回日期:2020-06-11 出版日期:2020-08-25 发布日期:2020-09-01
  • 通讯作者: 魏德宏。E-mail:weidh2011@163.com E-mail:weidh2011@163.com
  • 作者简介:陈鑑华(1997-),男,硕士,主要从事大地测量数据处理。E-mail:gdut_cjh@163.com
  • 基金资助:
    国家自然科学基金(41674006)

Correction of system bias between large-area GNSS leveling and gravity field model

CHEN Jianhua, WEI Dehong, ZHANG Xingfu, LI Weichao   

  1. Departments of Surveying and Mapping, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2020-04-24 Revised:2020-06-11 Online:2020-08-25 Published:2020-09-01

摘要: 大范围GNSS水准数据是评估重力场模型精度的重要独立数据源,通常大范围GNSS水准数据与地球重力场模型所对应的大地水准面不一致,导致两者间会存在系统偏差,该系统偏差会影响直接利用GNSS水准数据评估重力场模型精度的效果。本文以利用美国24 152个GNSS水准数据评估EIGEN-6C4重力场模型精度为例,提出采用分区常系统偏差校正法和四、五、七参数校正法校正两者间的系统偏差。试验结果表明,分区常系统偏差校正法和四、五、七参数校正法均可以有效校正两者间的系统偏差,系统偏差校正后,2160阶次的EIGEN-6C4模型在美国区域内的高程异常精度优于10 cm。

关键词: GNSS/水准, 重力场模型, 精度评估, 系统偏差, 参数法

Abstract: Large-area GNSS leveling data is important independent data sources for estimating the accuracy of the gravity field model. Generally, the system bias is caused by the geoid difference between the large-area GNSS leveling and gravity field model, which has an effect on estimating the accuracy of the gravity field model by directly using GNSS leveling data. Taking the 24 152 US GNSS leveling data to estimate EIGEN-6C4 model as an example, this paper proposes to use the divisional constant system bias correction method and the four/five/seven -parameters correction method to correct the system bias between GNSS leveling and gravity field model. The results show that the system bias can be determined and effectively corrected by using the correction method of the bias of the constant system and four/five/seven-parameters method. After correcting the system bias, the accuracy of 2160 degree EIGEN-6C4 model in the US is better than 10 cm.

Key words: GNSS leveling, gravity field model, accuracy estimation, systematic bias, method of parameters

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