Author(s)

Quancheng Wang ¹, Ye Yu ¹, Guodong Jin ¹, Jianwei Zhao ¹, Xiaoyu Gao ¹, Minli Yao ¹

Affiliation(s)

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

Corresponding Author

Ye Yu

ABSTRACT

This study proposes a GM-RBF composite model addressing limitations of standalone GM in satellite clock offset prediction. By synergistically combining GM-based trend extraction with RBF neural network residual modelling, the hybrid approach leverages minimal data requirements while enhancing predictive precision. Utilizing precise BDS ephemeris from Wuhan University, comparative experiments against GM, LPM and QPM benchmarks were conducted. The GM-RBF model achieved substantial improvements in 6-hour forecasting performance, with accuracy gains of 1.25–1.81 ns and stability enhancements of 0.25–1.73 ns. These results validate the superiority of component-based decomposition strategies for navigation satellite clock offset prediction.

KEYWORDS

BeiDou Satellite Navigation System; clock offset prediction; RBF neural network; grey model; accuracy analysis; stability analysis

CITE THIS PAPER

Quancheng Wang, Ye Yu, Guodong Jin, Jianwei Zhao, Xiaoyu Gao, Minli Yao. Satellite Clock Offset Prediction Using a Combined Model of GM and RBF Neural Network. Journal of Engineering Mechanics and Machinery (2026). Vol. 11, No. 1, 86-95. DOI: http://dx.doi.org/10.23977/jemm.2026.110109.

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