Missing Data Imputation in GNSS Monitoring Time Series Using Temporal and Spatial Hankel Matrix Factorization

GNSS time series for static reference stations record the deformation of monitored targets. However, missing data are very common in monitoring because receiver crashes, power failures, etc. In this paper, we propose a Temporal and Spatial Hankel Matrix Factorization (TSHMF) method that can simultaneously consider temporal correlation single spatial among different stations. Moreover, is verified using real-world regional 10-year period coordinate series. The Mean Absolute Error (MAE) Root-Mean-Square (RMSE) calculated to compare performance TSHMF with benchmark methods, which include time-mean, station-mean, K-nearest neighbor, singular value decomposition methods. results show reduce MAE range from 32.03% 12.98% RMSE 21.58% 10.36%, proving effectiveness proposed method.