Liu Bin, Xing Xuemin, Tan Jianbo, Xia Qing
Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science & Technology, Changsha 410114, China.
School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410014, China.
Sensors (Basel). 2020 Oct 1;20(19):5627. doi: 10.3390/s20195627.
Common seasonal variations in Global Positioning System (GPS) coordinate time series always exist, and the modeling and correction of the seasonal signals are helpful for many geodetic studies using GPS observations. A spatiotemporal model was proposed to model the common seasonal variations in vertical GPS coordinate time series, based on independent component analysis and varying coefficient regression method. In the model, independent component analysis (ICA) is used to separate the common seasonal signals in the vertical GPS coordinate time series. Considering that the periodic signals in GPS coordinate time series change with time, a varying coefficient regression method is used to fit the separated independent components. The spatiotemporal model was then used to fit the vertical GPS coordinate time series of 262 global International GPS Service for Geodynamics (IGS) GPS sites. The results show that compared with least squares regression, the varying coefficient method can achieve a more reliable fitting result for the seasonal variation of the separated independent components. The proposed method can accurately model the common seasonal variations in the vertical GPS coordinate time series, with an average root mean square (RMS) reduction of 41.6% after the model correction.
全球定位系统（GPS）坐标时间序列中始终存在常见的季节性变化，对季节性信号进行建模和校正有助于许多利用GPS观测的大地测量研究。基于独立成分分析和变系数回归方法，提出了一种时空模型来模拟垂直GPS坐标时间序列中的常见季节性变化。在该模型中，独立成分分析（ICA）用于分离垂直GPS坐标时间序列中的常见季节性信号。考虑到GPS坐标时间序列中的周期性信号随时间变化，采用变系数回归方法对分离出的独立成分进行拟合。然后利用该时空模型对262个全球国际地球动力学GPS服务（IGS）GPS站点的垂直GPS坐标时间序列进行拟合。结果表明，与最小二乘回归相比，变系数方法对分离出的独立成分的季节性变化能取得更可靠的拟合结果。所提方法能够准确地模拟垂直GPS坐标时间序列中的常见季节性变化，模型校正后平均均方根（RMS）降低了41.6%。
