Analysis of the regional GNSS coordinate time series by ICA-weighted spatio-temporal filtering

Independent component analysis (ICA) is a blind source signal separation method which can effectively estimate high-order information and thus can effectively extract the common mode errors (CMEs) of a regional global navigation satellite system (GNSS) observation network. In this paper, ICA is used for the weighted filtering (WICA) and the extraction of CMEs of a regional GNSS observation network with the root mean square error (RMSE) of daily solution taken as the weighting factor. Through an analysis of the observed data from 19 valid stations of the Crustal Movement Observation Network of China (CMONOC) in North China, it is shown that the coordinate series precision of 13, 16 and 12 stations in the N, E and U directions, respectively, after filtering by WICA is higher than that by the traditional ICA method. The average correlation coefficient of the coordinate time series for each station after filtering is obviously decreased. Two simulation experiments are designed to extract known CMEs. It is shown that CMEs can be recovered better by WICA and that the standard deviations of most stations after filtering are smaller than those by ICA. The results from the real data and simulation experiments suggest that the RMSE of coordinate series be considered in spatio-temporal filtering.