Abstract
Position, velocity, and timing (PVT) information is fundamental for the operation of large-scale Internet-of- Things (IoT) devices and services. Among various Global Navigation Satellite System (GNSS) positioning methods, precise point positioning (PPP) has emerged as a highly effective stand-alone solution, providing accurate and consistent performance over wide areas, and showing increasing potential for implementation on smartphones. This review focuses on smartphone-based PPP, beginning with a comparison of the positioning accuracies of primary built-in sensors commonly used for mobile localization. Key PPP processing techniques are then summarized, including preprocessing, stochastic modeling, error sources, and solution algorithms. We also introduce the classification of PPP methods and discuss their specific advantages when applied to smartphones. In addition, recent advancements over the past six years aimed at improving positioning accuracy are reviewed, including strategies for internal data integration, external data assistance, and optimization of internal models. Finally, major challenges and promising research directions in smartphone PPP are addressed, offering guidance for future development and applications of high-precision positioning in smartphones.
| Original language | English |
|---|---|
| Pages (from-to) | 13-34 |
| Number of pages | 22 |
| Journal | IEEE Sensors Journal |
| Volume | 26 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2026 |
Keywords
- Error sources
- machine learning (ML)
- precise point positioning (PPP)
- real-time kinematic (RTK) positioning
- smartphone positioning
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