融合GNSS和InSAR数据提高InSAR监测精度

Fusing GNSS and InSAR Data to Improve InSAR Monitoring Accuracy

GNSS数据反演的生成大气延迟或卫星轨道误差模型能够改正InSAR监测精度,但是大气延迟或卫星轨道误差模型需要用GNSS数据进行反演。GNSS数据反演的模型不仅计算量大,而且过程复杂。为了解决GNSS数据改正Insar误差的缺点,提出使用Kriging Kalman Filter模型融合GNSS和Insar数据的方法。该方法考虑了GNSS和InSAR数据的时空相关性,从而提高Insar监测精度。在GNSS和Insar数据融合实验中,融合沉降时间序列数据相比原始Insar沉降时间序列数据更趋近GNSS参考站的沉降时间趋势。

The atmospheric delay or satellite orbit error model generated by GNSS data inversion can correct the In SAR monitoring accuracy,but the atmospheric delay or satellite orbit error model needs to be inverted with GNSS data.The model of GNSS data inversion is not only computationally intensive,but also complicated.In order to avoid the shortcomings of the GNSS inversion model,proposes a method to fuse GNSS and Insar data using the Kriging Kalman Filter model.This method takes into account the temporal and spatial correlation of GNSS and In SAR data,thereby improving the accuracy of Insar monitoring.In the GNSS and Insar data fusion experiment,the fused settlement time series data is closer to the settlement time trend of the GNSS reference station than the original Insar settlement time series data.