Journal
SURVEY REVIEW
Volume 55, Issue 389, Pages 127-136Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00396265.2022.2025714
Keywords
Satellite clock bias forecast; Empirical mode decomposition; Quadratic polynomial; Permutation entropy; Pearson correlation coefficient
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In this study, a hybrid forecast model CEEMD-QP was proposed to eliminate the nonlinear and nonstationary characteristics of satellite clock bias. The experimental results showed that the CEEMD-QP method had obvious advantages in accuracy and stability for short-term forecast.
The nonlinear and nonstationary characteristics of satellite clock bias (SCB) have a harmful effect on the accuracy and stability of SCB forecast. To eliminate the influence of nonlinearity and non-stationarity, a hybrid forecast model was constructed that combines complementary ensemble empirical mode decomposition (CEEMD) and quadratic polynomial (QP), called CEEMD-QP. First, the SCB sequence is decomposed into several intrinsic mode function (IMF) components and one residual term by CEEMD. Second, permutation entropy (PE) and the correlation coefficient are used to quantitatively determine the IMF component with more noise and weak correlation with the original SCB signal. Finally, the SCB is reconstructed with the IMFs and residual components, and QP model is used to fit and forecast the clock bias. We adapt the observation part of ultra-rapid precise SCB data of GPS provided by IGS to forecast experiments. The results show that the CEEMD-QP method has obvious advantages of forecast accuracy and stability in short-term forecast.
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