BDS satellite clock offset prediction based on a semiparametric adjustment model considering model errors

Author:

Yan XiongORCID,Li Wentao,Yang Yufeng,Pan Xiong

Abstract

AbstractIn view of the influence of model errors in conventional BeiDou prediction models for clock offsets, a semiparametric adjustment model for BeiDou Navigation Satellite System (BDS) clock offset prediction that considers model errors is proposed in this paper. First, the model errors of the conventional BeiDou clock offset prediction model are analyzed. Additionally, the relationship among the polynomial model, polynomial model with additional periodic term correction, and its periodic correction terms is explored in detail. Second, considering the model errors, combined with the physical relationship between phase, frequency, frequency drift, and its period in the clock sequence, the conventional clock offset prediction model is improved. Using kernel estimation and comprehensive least squares, the corresponding parameter solutions of the prediction model and the estimation of its model error are derived, and the dynamic error correction of the clock sequence model is realized. Finally, the BDS satellite precision clock data provided by the IGS Center of Wuhan University with a sampling interval of 5 min are used to compare the proposed prediction method with commonly used methods. Experimental results show that the proposed prediction method can better correct the model errors of BDS satellite clock offsets, and it can effectively overcome the inaccuracies of clock offset correction. The average forecast accuracies of the BeiDou satellites at 6, 12, and 24 h are 27.13%, 37.71%, and 45.08% higher than those of the conventional BeiDou clock offset forecast models; the average model improvement rates are 16.92%, 20.96%, and 28.48%, respectively. In addition, the proposed method enhances the existing BDS satellite prediction method for clock offsets to a certain extent.

Funder

China University of Geosciences, Wuhan

Publisher

Springer Science and Business Media LLC

Reference22 articles.

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