That matters because conventional Precise Point Positioning (PPP) still struggles with time. Even when accurate enough, it often takes too long to converge, partly because ionospheric effects are difficult to model cleanly. The standard Single Layer Model (SLM) mapping function remains widely used, but it is less effective where Total Electron Content (TEC) changes rapidly across space, especially at low latitudes. As global ionospheric maps improve, the mapping function itself becomes a more important source of error. Due to these challenges, there is a need to carry out in-depth research on better ionospheric mapping methods that can support faster, more reliable Precise Point Positioning with integer Ambiguity Resolution (PPP-AR) performance.
Researchers from the University of Warmia and Mazury in Olsztyn (UWM), the German Aerospace Center (DLR), DLR Gesellschaft für Raumfahrtanwendungen mbH (GfR), and the European Space Agency (ESA) reported (DOI: 10.1186/s43020-026-00193-0) in 2026 in Satellite Navigation that they had validated a new multi-layer ionospheric mapping function for PPP-AR. The study tested whether replacing the conventional single-shell assumption with a layered ionosphere model could improve how GNSS users convert Vertical Total Electron Content (VTEC) information into slant corrections that are directly useful for positioning.
The team evaluated the method in two ways. First, they tested it in the positioning domain using an uncombined PPP-AR model. Second, they checked it in the Global Navigation Satellite System (GNSS) observation domain by comparing converted Slant Total Electron Content (STEC) values with benchmark estimates from the Geometry-Free (GF) linear combination. The dataset included nine permanent GNSS stations spanning high, middle, and low latitudes, with winter and summer test periods in 2019. Across most stations and periods, the multi-layer mapping functions outperformed the conventional SLM. On average, the PPP-AR filter converged 4–10% faster when the multi-layer approach was used. In the summer 2019 tests, mean convergence time across all stations fell from 11.4 minutes with SLM to 10.3 minutes with the Multi-Layer (ML) blind model; in winter 2019, it fell from 14.3 to 13.8 minutes. The benefit was strongest near the equator. At the BOAV station, for example, mean convergence time dropped from 21.7 minutes to 19.2 minutes when the ML blind mapping function replaced the conventional model. The new method also improved early-stage positioning: averaged across all stations, the mean three-dimensional Root Mean Square (RMS) error in the second epoch decreased from 0.529 m to 0.518 m in summer 2019, while in winter 2019 it decreased from 0.569 m to 0.558 m.
“This is the kind of improvement that matters precisely because it appears at the start,” the study suggests. “In high-precision GNSS, the first minutes often determine whether a method feels practical or frustrating. By making ionospheric corrections more faithful to reality, the multi-layer approach helps PPP-AR settle faster and behave better where the ionosphere is most difficult—especially in equatorial skies.”
The implications are broader than one correction model. Faster convergence can make PPP-AR more attractive for real-world navigation, surveying, and other applications that cannot afford long initialization delays. The study also shows where future gains are likely to matter most: low-latitude regions, where conventional assumptions break down fastest. Even the computational cost remained manageable, with the ML blind approach requiring only about 10% more processing time than SLM. In other words, this is not just a theoretical refinement. It is a practical case for how better ionospheric structure can translate into better positioning performance.
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References
DOI
10.1186/s43020-026-00193-0
Original Source URL
https://doi.org/10.1186/s43020-026-00193-0
Funding Information
The work is funded by the LMAP (LEO Ionospheric Mapping Assessment and Derivation For Precise PVT Applications) project under the ESA Contract No. 4000142821/23/NL/MGu/my and in part by the National Science Centre, Poland, Project No. 2023/48/Q/ST10/00059.
About Satellite Navigation
Satellite Navigation (E-ISSN: 2662-1363; ISSN: 2662-9291) is the official journal of Aerospace Information Research Institute, Chinese Academy of Sciences. The journal aims to report innovative ideas, new results or progress on the theoretical techniques and applications of satellite navigation. The journal welcomes original articles, reviews and commentaries.