The existing spatiotemporal analysis methods suppose that the involved time series are complete and have the same data interval. However missing data inevitably occur in the position time series of Global Navigation Satellite Systems networks for many reasons. In this paper, we develop a modified principal component analysis to extract the Common Mode Error (CME) from the incomplete position time series. The principle of the proposed method is that a time series can be reproduced from its principle components.
Shen, Y., Li, W., Xu, G., Li, B. Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis,
Springer Berlin Heidelberg, 2014, с.. 1-12.
Shen, Y., Li, W., Xu, G., Li, B. .
Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis.
: Springer Berlin Heidelberg, 2014, с.. 1-12.
Shen, Y., Li, W., Xu, G., Li, B. (2014)
Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis,
: Springer Berlin Heidelberg, с.. 1-12
Shen, Y.,
Li, W.,
Xu, G., &
Li, B.
(2014).
Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis. Journal of Geodesy. Springer Berlin Heidelberg 88 (1), с.. 1-12.
