Abstract

In order to improve the accuracy of satellite clock bias (SCB) prediction, a combined model is proposed. In combined model, polynomial model is used to extract the trend of SCB, which can enhance relevance of data and improve efficiency of ensemble empirical mode decomposition (EEMD). Simultaneously, residual data is decomposed into several intrinsic mode functions (IMFs) and a remainder term according to EEMD. Principal component analysis (PCA) is introduced to distinguish IMFs using frequency as a reference, and high-frequency sequence is the sum of IMFs with high frequency, low-frequency sequence is the sum of IMFs with low frequency and the remainder term. Meanwhile, LSSVM model is employed to predict the high-frequency sequence, and other sequence is predicted by GM(1,1) model. The final consequence is the combination of these two models and the SCB’s trend. SCBs from four different satellites are selected to evaluate the performance of this combined model. Results show that combined model is superior to conventional model both in 6- and 24-h prediction. Especially, as for Cs clock, it achieves 6-h prediction error less than 3 ns, and 24-h prediction error less than 8 ns.

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