ArticleSensors (Basel, Switzerland)2026
A Method for Enhancing the Positioning Performance of PPP-B2b by Integrating Galileo Observation.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The BeiDou-3 (BDS-3) Precise Point Positioning service (PPP-B2b) can realize decimeter-level positioning by broadcasting satellite orbit, clock offset, and code bias corrections via GEO satellites, enabling PPP without reliance on ground communication networks. However, the current PPP-B2b service only provides corrections for BDS-3 and GPS satellites, which limits the number of available satellites and may affect positioning performance in challenging environments. To further enhance the positioning performance, we propose to incorporate Galileo observation into the PPP-B2b positioning. A PPP model integrating PPP-B2b service and broadcast ephemeris was established. First, the accuracy of the Galileo broadcast ephemeris was evaluated using precise orbit and clock products as references. The results show that the mean signal-in-space range error (SISRE) standard deviation of Galileo broadcast ephemeris is 0.30, which is only a little worse than that of GPS from PPP-B2b service. Then, the positioning experiments were conducted under different elevation cutoff angles. The experiments were conducted using data from 94 reference stations in China over a 7-day period. The results demonstrate that the inclusion of Galileo satellites significantly increases the number of visible satellites and improves satellite geometry. Compared with the BDS-3/GPS dual-system PPP solution, the BDS-3/GPS/Galileo triple-system PPP solution reduces the horizontal convergence time by approximately 13.70-16.67% and the vertical convergence time by about 18.75-20.00% under cutoff angles from 7° to 30° based on the 68th percentile statistics. The 95th percentile results further confirm the advantage of the triple-system solution under a more stringent statistical criterion. Where convergence is achieved, the triple-system solution reduces the horizontal convergence time by approximately 6.0-7.3% and the vertical convergence time by about 15.3-26.0%. Moreover, the triple-system solution exhibits a smaller re-convergence jump under abnormal observation conditions. In addition, under high elevation cutoff conditions, the introduction of Galileo satellites effectively improves PPP availability, thereby enhancing the continuity and robustness of PPP. These results indicate that incorporating Galileo observation within the PPP-B2b framework can effectively improve PPP performance and provide a simple and practical approach for high-precision real-time positioning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.