Li Aiguo, Wang Yifan, Guo Min
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China.
Sensors (Basel). 2024 Sep 3;24(17):5731. doi: 10.3390/s24175731.
When analyzing GPS time series, common mode errors (CME) often obscure the actual crustal movement signals, leading to deviations in the velocity estimates of station coordinates. Therefore, mitigating the impact of CME on station positioning accuracy is crucial to ensuring the precision and reliability of GNSS time series. The current approach to separating CME mainly uses signal filtering methods to decompose the residuals of the observation network into multiple signals, from which the signals corresponding to CME are identified and separated. However, this method overlooks the spatial correlation of the stations. In this paper, we improved the Independent Component Analysis (ICA) method by introducing correlation coefficients as weighting factors, allowing for more accurate emphasis or attenuation of the contributions of the GNSS network's spatial distribution during the ICA process. The results show that the improved Weighted Independent Component Analysis (WICA) method can reduce the root mean square (RMS) of the coordinate time series by an average of 27.96%, 15.23%, and 28.33% in the E, N, and U components, respectively. Compared to the ICA method, considering the spatial distribution correlation of stations, the improved WICA method shows enhancements of 12.53%, 3.70%, and 8.97% in the E, N, and U directions, respectively. This demonstrates the effectiveness of the WICA method in separating CMEs and provides a new algorithmic approach for CME separation methods.
在分析GPS时间序列时，共模误差（CME）常常会掩盖实际的地壳运动信号，导致台站坐标速度估计出现偏差。因此，减轻CME对台站定位精度的影响对于确保GNSS时间序列的精度和可靠性至关重要。目前分离CME的方法主要是利用信号滤波方法将观测网络的残差分解为多个信号，从中识别并分离出与CME对应的信号。然而，这种方法忽略了台站的空间相关性。在本文中，我们通过引入相关系数作为加权因子对独立分量分析（ICA）方法进行了改进，使得在ICA过程中能够更准确地强调或减弱GNSS网络空间分布的贡献。结果表明，改进后的加权独立分量分析（WICA）方法在东、北、天分量上分别能使坐标时间序列的均方根（RMS）平均降低27.96%、15.23%和28.33%。与ICA方法相比，考虑台站的空间分布相关性后，改进后的WICA方法在东、北、天方向上分别提高了12.53%、3.70%和8.97%。这证明了WICA方法在分离CME方面的有效性，并为CME分离方法提供了一种新的算法途径。
