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Satellite Clock Bias Prediction Method for BeiDou-3 Satellites Based on Entropy Weight Method

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DOI: 10.23977/jeis.2025.100108 | Downloads: 16 | Views: 599

Author(s)

Chaopan Yang 1, Ye Yu 1, Weimin Jia 1, Guodong Jin 1, Yihong Li 1, Wei Jin 1, Jianwei Zhao 1

Affiliation(s)

1 Rocket Force University of Engineering, Xi'an, 710025, Shaanxi, China

Corresponding Author

Ye Yu

ABSTRACT

In order to improve the accuracy and stability of satellite clock bias prediction, a combined satellite clock bias prediction method based on the entropy weight method is proposed. Firstly, the method adopts a quadratic polynomial model and a gray model to make a single prediction of satellite clock bias and generate two sets of prediction results. Then, by calculating the entropy of error information of the two sets of prediction results, it determines the weights of each model and realizes the optimal fusion of the models. Finally, the entropy weight combination method is used to obtain a higher precision prediction result. Four different types of BeiDou-3 satellites were randomly selected for the prediction test by using the precision satellite clock bias products released by the GNSS Analysis Center of Wuhan University. The results show that the method can provide high-precision short- and medium-term predictions of BeiDou-3 satellite clock bias, and its 6-h average prediction accuracy and stability are 0.22ns and 0.46ns, respectively, which are 72.15% and 48.84% higher than the average prediction accuracy of quadratic polynomial and gray models, and the stability is 70.00% and 20 .69% higher, respectively.

KEYWORDS

Quadratic Polynomial Model, Gray Model, Entropy Weighting Method, Satellite Clock Bias, Prediction

CITE THIS PAPER

Chaopan Yang, Ye Yu, Weimin Jia, Guodong Jin, Yihong Li, Wei Jin, Jianwei Zhao, Satellite Clock Bias Prediction Method for BeiDou-3 Satellites Based on Entropy Weight Method. Journal of Electronics and Information Science (2025) Vol. 10: 62-69. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2025.100108.

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