Helmert transformation strategies in analysis of GPS position time-series
Helmert transformation strategies in analysis of GPS position time-series
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GPS位置时间序列分析中的Helmert变换策略
DOI：
10.1093/gji/ggaa371
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发表时间：
2020-11
影响因子：
2.8
通讯作者：
Zheng Fu
中科院分区：
文献类型：
--
作者：
Guo Shiwei;Shi Chuang;Wei Na;Li Min;Fan Lei;Wang Cheng;Zheng Fu
利用不一致卫星产品生成的全球定位系统（GPS）位置时间序列，需要通过Helmert变换将其对准长期地面参考系。然而，未建模的站位非线性变化可以混叠成转换参数。基于高精度点定位（PPP）产生的112个站点17年的位置时间序列，研究了网络配置和尺度因子对长期时间序列处理的影响。无论尺度因子是否估计，相对于均匀网络，不均匀网络的平移、旋转和尺度因子（如果估计）的均方根（RMS）差异分别为0.7 ~ 1.1 mm、21.3 ~ 27.5 μas和1.3 mm。这种网络效应引起的垂直年振幅差的均方根值达到0.5 ~ 0.6 mm。是否估计尺度因子主要影响z向平移和垂直年幅值，导致使用不均匀网时的差异为1.3 mm。同时，尺度因子引起的年幅值差异在北、东、上分量上呈现出不同的地理位置依赖性。使用均匀网络而不估计比例因子的转换得到的季节信号与地表质量负荷具有更好的一致性，可以解释超过41%的垂直年变化。模拟研究表明，尺度因子中40 - 50%的年度信号可以用地表质量负荷的混叠来解释。另一个发现是站位GPS天线误差也可以混叠到变换参数中，而不同的变换策略对天线误差识别的影响有限。我们建议在Helmert变换中使用均匀网络，不需要估计尺度因子。由于从卫星轨道和时钟继承的PPP位置的起源在很长一段时间内不是那么稳定，因此即使使用的卫星产品属于一致的参考帧，也建议对PPP时间序列进行帧对齐。采用Helmert变换后，季节变化更符合地表质量负荷，降低了时间序列的噪声水平。
Global positioning system (GPS) position time-series generated using inconsistent satellite products should be aligned to a secular Terrestrial Reference Frame by Helmert transformation. However, unmodelled non-linear variations in station positions can alias into transformation parameters. Based on 17 yr of position time-series of 112 stations produced by precise point positioning (PPP), we investigated the impact of network configuration and scale factor on long-term time-series processing. Relative to the uniform network, the uneven network can introduce a discrepancy of 0.7–1.1 mm, 21.3–27.5 μas and 1.3 mm in terms of root mean square (RMS) for the translation, rotation and scale factor (if estimated), respectively, no matter whether the scale factor is estimated. The RMS of vertical annual amplitude differences caused by such network effect reaches 0.5–0.6 mm. Whether estimating the scale factor mostly affects the Z-translation and vertical annual amplitude, leading to a difference of 1.3 mm when the uneven network is used. Meanwhile, the annual amplitude differences caused by the scale factor present different geographic location dependences over the north, east and up components. The seasonal signals derived from the transformation using the uniform network and without estimating scale factor have better consistency with surface mass loadings with more than 41 per cent of the vertical annual variations explained. Simulation studies show that 40–50 per cent of the annual signals in the scale factor can be explained by the aliasing of surface mass loadings. Another finding is that GPS draconitic errors in station positions can also alias into transformation parameters, while different transformation strategies have limited influence on identifying the draconitic errors. We suggest that the uniform network should be used and the scale factor should not be estimated in Helmert transformation. It is also suggested to perform frame alignment on PPP time-series, even though the used satellite products belong to a consistent reference frame, as the origin of PPP positions inherited from satellite orbits and clocks is not so stable during a long period. With Helmert transformation, the seasonal variations would better agree with surface mass loadings, and noise level of time-series is reduced.
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影响因子：
5.2
作者：
Lagler, K.;Schindelegger, M.;Boehm, J.;Krasna, H.;Nilsson, T.
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影响因子：
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