基于稳定度加权和Kalman滤波算法相融合的多级时间比对融合算法

doi:10.12305/j.issn.1001-506X.2026.08.28

Abstract

Abstract:

For the single techniques such as two-way satellite time and frequence transfer （TWSTFT） and precise point positioning （PPP）, which are difficult to satisfy the demands of high accuracy and high stability at the same time, a multi-level time comparison fusion algorithm is proposed. Model 1 fuses TWSTFT, GALILEO P3, and global positioning system （GPS） P3 codes with stability weighting, and then fuses the weighted results with GPS PPP using Kalman filtering, while Model 2 prioritizes the fusion of TWSTFT and GPS PPP, and then fuses GALILEO P3 and GPS P3 codes step by step by two-level Kalman filtering. Simulation results show that both models can improve the accuracy and stability of time comparison, and Model 1 is suitable for long-term clock difference analysis scenarios with high stability requirements, while Model 2 is suitable for real-time monitoring and time comparison needs in dynamic environments. The data failure test further shows that Model 2 performs better when GPS PPP is missing, and both models show good fault tolerance for GALILEO P3 and GPS P3 code missing, which verifies the fault tolerance of the algorithm design.

Key words: time comparison, data fusion, Kalman filtering, stability weighting

CLC Number:

Xueyou NING, Qiang LIU, Denghua HU, Shuang ZHANG. Multi-level time comparsion fusion algorithm based on the intergration of stability weighting and Kalman filtering algorithm[J]. Systems Engineering and Electronics, 2026, 48(8): 2833-2840.

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References 28

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链路	最大值	最小值	均值	标准差
TWSTFT-GPS PPP	1.894 4	0.144 3	0.985 0	0.303 6
GPS P3-GPS PPP	2.170 6	−2.815 2	−0.241 8	1.017 6
GALILEO P3-GPS PPP	2.274 3	−0.283 2	1.029 4	0.397 1
模型一-GPS PPP	0.198 4	−0.163 1	0.029 0	0.054 2
模型二-GPS PPP	0.325 4	−0.073 6	0.143 9	0.064 1

模型	单一数据缺失	均方根误差	最大偏移量
模型一	GPS P3缺失	0.002 8	0.134 9
	GPS PPP缺失	0.632 3	1.072 8
	GALILEO P3缺失	0.000 4	0.073 2
模型二	GPS P3缺失	0.007 9	0.522 5
	GPS PPP缺失	0.153 2	0.755 5
	GALILEO P3缺失	0.001 2	0.098 9

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Multi-level time comparsion fusion algorithm based on the intergration of stability weighting and Kalman filtering algorithm

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