在小规模GNSS网络中对空间分布和常见模式错误相关性的分析
Aiguo Li1, Yifan Wang1, Min Guo1
1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
一种新的权重独立组件分析 (WICA) 方法有效地将GPS数据中的常见模式错误 (CME) 分开. 这提高了地运动分析的准确性,因为它考虑了空间站的相关性.
科学领域:
- 地质测量是指地质测量.
- 地质物理学 地质物理学
- 信号处理 信号处理
背景情况:
- 全球导航卫星系统 (GNSS) 数据中的常见模式错误 (CME) 隐藏了地运动信号.
- 准确的速度估计站坐标对于可靠的地质测量非常重要.
- 目前使用信号过的CME分离方法往往忽视了站点之间的空间相关性.
研究的目的:
- 通过开发一种用于常态错误分离的新方法来提高GNSS时间序列分析的准确性.
- 通过纳入GNSS网络的空间信息来解决现有方法的局限性.
- 通过减轻CME影响,提高站位定位的精度和可靠性.
主要方法:
- 通过引入相关系数作为加权因子来改进独立成分分析 (ICA) 方法.
- 开发加权独立组件分析 (WICA) 方法,以计算GNSS站的空间分布.
- 观察网络残留物的分解,以识别和分离CME信号.
主要成果:
- 通过WICA方法,坐标时间序列的平方根平均值 (RMS) 平均减少了27.96% (东),15.23% (北) 和28.33% (上).
- 与标准ICA相比,WICA的改善率为12.53% (东部),3.70% (北部) 和8.97% (上部).
- 结果表明WICA在分离常见模式错误方面的有效性.
结论:
- 权重独立组件分析 (WICA) 方法为GNSS数据中的常态错误分离提供了更准确的方法.
- 与传统的ICA相比,将站点的空间相关性纳入其中显著提高了CME分离.
- 这项研究提供了一种新的算法解决方案,用于提高测量测量和地运动分析的精度.
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