Smartphones rely heavily on Global Navigation Satellite System (GNSS) for navigation and location-based services, yet their positioning accuracy degrades sharply in dense urban areas. Tall buildings block satellite signals, generate reflections, and distort pseudorange measurements, leading to frequent cross-street errors, trajectory jumps, and loss of continuity. Existing mitigation strategies—such as robust estimation, machine learning-based signal classification, or traditional 3D map-aided methods—either depend on ideal assumptions or struggle to resolve ambiguities under complex urban geometry. Meanwhile, high-precision GNSS observables such as carrier phase are underutilized on smartphones due to frequent signal interruptions. Based on these challenges, there is a strong need to develop a more robust, map-assisted and multi-observation positioning framework for urban smartphone navigation.
Researchers from the China University of Mining and Technology and Shandong Jianzhu University reported a new smartphone positioning strategy in Satellite Navigation, published (DOI: 10.1186/s43020-025-00185-6) in 2025. The study introduces a tightly coupled factor graph optimization framework that fuses 3D city maps with multiple GNSS observations, including pseudorange, Doppler, and time-differenced carrier phase measurements. By integrating spatial constraints from urban maps with high-precision temporal information, the method substantially improves positioning accuracy and trajectory continuity in dense urban environments, where conventional smartphone GNSS techniques often fail.
The proposed framework tackles urban positioning errors at both the spatial and temporal levels. First, the researchers enhanced traditional shadow matching by introducing time-differenced carrier phase (TDCP) constraints into satellite visibility scoring. This allows candidate positions to be evaluated not only by signal consistency with surrounding buildings, but also by short-term motion direction and displacement, reducing ambiguity along streets. Second, a probabilistic map-matching strategy identifies the most likely road segment at each epoch, effectively narrowing the search space and mitigating cross-street positioning errors.
To further resolve multimodal candidate distributions, the study applies a RANSAC-based clustering method that selects a unique, physically meaningful solution rather than relying on heuristic weighted averages. These spatial constraints are then integrated into a factor graph optimization framework that fuses pseudorange, Doppler, TDCP, and clock-related constraints across consecutive epochs.
Field experiments conducted in a dense campus environment with severe signal obstruction show clear performance gains. The proposed method achieved horizontal positioning errors within 3 m and 5 m for 76.7% and 93.1% of epochs, respectively—substantially outperforming advanced GNSS multi-source fusion approaches. The resulting trajectories were smoother, more continuous, and better aligned with actual pedestrian paths, even in heavily obstructed urban corridors.
According to the research team, the key advance lies in fully exploiting high-precision GNSS information that is often overlooked in smartphone positioning. By using time-differenced carrier phase to strengthen motion constraints and combining it with 3D map knowledge, the framework bridges the gap between absolute positioning and short-term relative accuracy. The researchers emphasize that this integrated strategy not only improves accuracy but also enhances reliability and continuity—two critical requirements for real-world navigation applications in complex urban environments.
Looking ahead, several research directions could further advance 3D map-aided smartphone GNSS positioning in complex urban environments. Higher-resolution and higher-level-of-detail 3D urban models would enable more accurate satellite visibility prediction and stronger geometric constraints, particularly in narrow urban canyons. In addition, developing globally reliable and confidence-aware TDCP acquisition strategies could improve the robustness of temporal constraints under signal interruptions common to smartphones. Finally, integrating GNSS with inertial sensors, vision, LiDAR, or other opportunistic smartphone sensors within a unified factor-graph framework could enhance positioning continuity during GNSS outages and improve robustness under severe occlusion. Together, these advances would support more resilient, accurate, and scalable smartphone-based positioning, with broad implications for urban navigation, location-based services, and future smart-city applications.
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References
DOI
10.1186/s43020-025-00185-6
Original Source URL
https://doi.org/10.1186/s43020-025-00185-6
Funding information
This work was supported in part by the National Natural Science Foundation of China (Grants 42394060, 42394065 and 42274020), the Science and Technology Planning Project of Jiangsu Province (Grant BE2023692). It was also supported by the Fundamental Research Funds for the Central Universities (Grants 2025-00046), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant KYCX25_2890), and the Graduate Innovation Program of China University of Mining and Technology (Grant 2025WLKXJ200).
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.