TY - JOUR
T1 - State-of-the-Art Precise Point Positioning and Its Applications on Smartphone
T2 - A Review
AU - Liu, Peng
AU - Qin, Honglei
AU - Lu, Jun
AU - Liu, Ran
AU - Liang Guan, Yong
AU - Yuen, Chau
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2026/1
Y1 - 2026/1
N2 - 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.
AB - 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.
KW - Error sources
KW - machine learning (ML)
KW - precise point positioning (PPP)
KW - real-time kinematic (RTK) positioning
KW - smartphone positioning
UR - https://www.scopus.com/pages/publications/105021415501
U2 - 10.1109/JSEN.2025.3629561
DO - 10.1109/JSEN.2025.3629561
M3 - 文献综述
AN - SCOPUS:105021415501
SN - 1530-437X
VL - 26
SP - 13
EP - 34
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 1
ER -