Investigating Ground Station Deployment Strategies to Improve Quality of Orbit Determination of Regional Navigation Satellite System
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Regional navigation satellite systems (RNSS) consist of geosynchronous orbit for servicing positioning, navigation and timing regionally. This presents a challenging environment for precise orbit determination (OD) as their regional ground networks provide limited line-of-sight diversity and weaken the observation geometry. To address these limitations, this study focuses on Japan’s Quasi-Zenith Satellite System (QZSS) as a representative RNSS and evaluates two practical improvement strategies: (i) augmenting the processing with GPS observations, and (ii) expanding the ground tracking network. Three network configurations are tested—an 11-station regional network (R11), a denser 20-station regional network (R20), and a 20-station global network (G20)—under both QZSS-only processing (J) and joint QZSS+GPS (GJ) processing. Orbit and clock solutions are evaluated against the product from the Multi-GNSS Experiment Project (MGEX) of the International GNSS Service (IGS) as a reference. Using R11-J (which is the most probable scenario and exhibits the largest QZSS 3D RMS in our scenarios) as the baseline, including GPS reduces the QZSS 3D RMS by about 70% and shows a clear geometric benefit. Without GPS, R20-J outperforms G20-J, which indicates that for RNSS the number of usable observations from a denser regional network dominates the advantage of global distribution. When GPS is incorporated, the overall optimum shifts to G20-GJ: adding GPS raises QZSS OD to the level achieved by R20-GJ, while the GPS orbits are substantially more accurate—the mean (over days) of the daily-median 3D RMS of GPS reaches ~6.7 cm, compared with ~23.1 cm under R20-GJ. The station-deployment choice is tightly coupled to the processing strategy. If stand-alone independence is required, densifying the regional network to ~20 stations is a more effective option; if GPS augmentation is acceptable, a globally distributed 20-station network is recommended for maximum overall performance.
Abstract
Regional navigation satellite systems (RNSS) consist of geosynchronous orbit for servicing positioning, navigation and timing regionally. This presents a challenging environment for precise orbit determination (OD) as their regional ground networks provide limited line-of-sight diversity and weaken the observation geometry. To address these limitations, this study focuses on Japan’s Quasi-Zenith Satellite System (QZSS) as a representative RNSS and evaluates two practical improvement strategies: (i) augmenting the processing with GPS observations, and (ii) expanding the ground tracking network. Three network configurations are tested—an 11-station regional network (R11), a denser 20-station regional network (R20), and a 20-station global network (G20)—under both QZSS-only processing (J) and joint QZSS+GPS (GJ) processing. Orbit and clock solutions are evaluated against the product from the Multi-GNSS Experiment Project (MGEX) of the International GNSS Service (IGS) as a reference. Using R11-J (which is the most probable scenario and exhibits the largest QZSS 3D RMS in our scenarios) as the baseline, including GPS reduces the QZSS 3D RMS by about 70% and shows a clear geometric benefit. Without GPS, R20-J outperforms G20-J, which indicates that for RNSS the number of usable observations from a denser regional network dominates the advantage of global distribution. When GPS is incorporated, the overall optimum shifts to G20-GJ: adding GPS raises QZSS OD to the level achieved by R20-GJ, while the GPS orbits are substantially more accurate—the mean (over days) of the daily-median 3D RMS of GPS reaches ~6.7 cm, compared with ~23.1 cm under R20-GJ. The station-deployment choice is tightly coupled to the processing strategy. If stand-alone independence is required, densifying the regional network to ~20 stations is a more effective option; if GPS augmentation is acceptable, a globally distributed 20-station network is recommended for maximum overall performance.