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Remote Sensing
Volume 17
Issue 21
10.3390/rs17213548
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Article

A Hybrid-Weight TOPSIS and Clustering Approach for Optimal GNSS Station Selection in Multi-GNSS Precise Orbit Determination

by
Weitong Jin
1,
Xing Li
2,3,*,
Liang Chen
2,3,
Chuanzhen Sheng
1,
Yongqiang Yuan
4,
Keke Zhang
4,
Xingxing Li
4,
Jingkui Zhang
1,
Xulun Zhang
1 and
Baoguo Yu
1
1
State Key Laboratory of Comprehensive PNT Network and Equipment Technology, The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
2
Beijing Institute of Tracking and Telecommunication Technology, Beijing 100094, China
3
National Key Laboratory of Intelligent Spatial Information, Beijing 100094, China
4
Hubei Luojia Laboratory, School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(21), 3548; https://doi.org/10.3390/rs17213548
Submission received: 29 September 2025 / Revised: 22 October 2025 / Accepted: 23 October 2025 / Published: 26 October 2025
(This article belongs to the Special Issue Beidou/GNSS Positioning, Navigation and Timing: Methods and Technology (Third Edition))
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Abstract

The accuracy of Precise Orbit Determination (POD) for Global Navigation Satellite Systems (GNSS) critically depends on optimal tracking station selection. This study proposed and validates a novel framework that integrates a hybrid-weight Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model with spherical k-means clustering, effectively resolving the challenge of balancing station data quality with uniform spatial distribution. The framework generates by first a comprehensive quality score for each station based on 40 indicators and then selects the top-scoring station from distinct geographical clusters to construct a well-distributed, high-quality network. To validate the methodology, we performed multi-GNSS POD using networks of 30, 60, and 90 stations selected by the proposed framework. The accuracy was assessed via two independent methods: orbit comparisons (Root Mean Square, RMS) against final Analysis Center (AC) orbits and Satellite Laser Ranging (SLR) validation. The results demonstrate that the optimized 60-station network (e.g., RMS of ~2.5, 5.3, 2.1, and 5.4 cm for GPS, GLONASS, Galileo, and BDS, respectively) achieves an accuracy comparable to that of a 90-station network. Moreover, a 30-station globally uniform network outperforms a 90-station network of high-quality but spatially clustered stations. This study provides an objective and quantitative solution for establishing efficient and reliable GNSS tracking networks, directly benefiting ACs and other high-precision applications.
Keywords: GNSS; precise orbit determination; TOPSIS; spherical k-means; optimal GNSS station selection GNSS; precise orbit determination; TOPSIS; spherical k-means; optimal GNSS station selection

Share and Cite

MDPI and ACS Style

Jin, W.; Li, X.; Chen, L.; Sheng, C.; Yuan, Y.; Zhang, K.; Li, X.; Zhang, J.; Zhang, X.; Yu, B. A Hybrid-Weight TOPSIS and Clustering Approach for Optimal GNSS Station Selection in Multi-GNSS Precise Orbit Determination. Remote Sens. 2025, 17, 3548. https://doi.org/10.3390/rs17213548

AMA Style

Jin W, Li X, Chen L, Sheng C, Yuan Y, Zhang K, Li X, Zhang J, Zhang X, Yu B. A Hybrid-Weight TOPSIS and Clustering Approach for Optimal GNSS Station Selection in Multi-GNSS Precise Orbit Determination. Remote Sensing. 2025; 17(21):3548. https://doi.org/10.3390/rs17213548

Chicago/Turabian Style

Jin, Weitong, Xing Li, Liang Chen, Chuanzhen Sheng, Yongqiang Yuan, Keke Zhang, Xingxing Li, Jingkui Zhang, Xulun Zhang, and Baoguo Yu. 2025. "A Hybrid-Weight TOPSIS and Clustering Approach for Optimal GNSS Station Selection in Multi-GNSS Precise Orbit Determination" Remote Sensing 17, no. 21: 3548. https://doi.org/10.3390/rs17213548

APA Style

Jin, W., Li, X., Chen, L., Sheng, C., Yuan, Y., Zhang, K., Li, X., Zhang, J., Zhang, X., & Yu, B. (2025). A Hybrid-Weight TOPSIS and Clustering Approach for Optimal GNSS Station Selection in Multi-GNSS Precise Orbit Determination. Remote Sensing, 17(21), 3548. https://doi.org/10.3390/rs17213548

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