Open Access

ARTICLE

A Method for Rapidly Determining the Optimal Distribution Locations of GNSS Stations for Orbit and ERP Measurement Based on Map Grid Zooming and Genetic Algorithm

Qianxin Wang^1,2,3, Chao Hu^1,2,*, Ya Mao^1,2

1 NASG Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou, 221116, China.
2 School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China.
3 Mathematical and Geospatial Sciences, RMIT University, Melbourne 3001, Australia.

*Corresponding Author: Chao Hu. Email: .

Computer Modeling in Engineering & Sciences 2018, 117(3), 509-525. https://doi.org/10.31614/cmes.2018.04098

Abstract

Designing the optimal distribution of Global Navigation Satellite System (GNSS) ground stations is crucial for determining the satellite orbit, satellite clock and Earth Rotation Parameters (ERP) at a desired precision using a limited number of stations. In this work, a new criterion for the optimal GNSS station distribution for orbit and ERP determination is proposed, named the minimum Orbit and ERP Dilution of Precision Factor (OEDOP) criterion. To quickly identify the specific station locations for the optimal station distribution on a map, a method for the rapid determination of the selected station locations is developed, which is based on the map grid zooming and heuristic technique. Using the minimum OEDOP criterion and the proposed method for the rapid determination of optimal station locations, an optimal or near-optimal station distribution scheme for 17 newly built BeiDou Navigation Satellite System (BDS) global tracking stations is suggested. To verify the proposed criterion and method, real GNSS data are processed. The results show that the minimum OEDOP criterion is valid, as the smaller the value of OEDOP, the better the precision of the satellite orbit and ERP determination. Relative to the exhaustive method, the proposed method significantly improves the computational efficiency of the optimal station location determination. In the case of 3 newly built stations, the computational efficiency of the proposed method is 35 times greater than that of the exhaustive method. As the number of stations increases, the improvement in the computational efficiency becomes increasingly obvious.

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Keywords

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