Factor Graph Optimization-Based GNSS PPP-RTK: An Alternative Platform to Study Urban GNSS Precise Positioning

• Published in: IEEE transactions on aerospace and electronic systems Vol. 60; no. 3; pp. 3221 - 3236
• Main Authors: Xin, Shaoming, Geng, Jianghui, Hsu, Li-Ta
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerospace and electronic systems; Atmospheric correction; Clocks; Delays; Global navigation satellite system; Kalman filters; Kinematics; Low cost; Optimization; Phase measurement; Real time; Receivers; Satellites; Servers; Street canyons; Urban environments
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2024.3360380

The global navigation satellite system (GNSS) PPP-RTK technique, which integrates the superiorities of precise point positioning (PPP) and real-time kinematic (RTK), is increasingly attracting people's attention in recent years. This article proposes a factor graph optimization (FGO) based GNSS PPP-RTK method to provide an alternative platform to study urban GNSS precise positioning. Instead of a batch FGO estimator, the proposed method uses the sliding-window method to meet the real-time requirement. Pseudorange, carrier-phase measurements, atmospheric corrections at each epoch, and Doppler velocity and atmospheric variations between the adjacent epochs are integrated to estimate receiver positions. We evaluated the performance of the proposed method by carrying out static and kinematic experiments in different scenarios. Two static experiments based on the data from a base station in an open-sky environment and a low-cost GNSS receiver in a typical urban environment can both obtain centimeter-level positioning results. Two kinematic experiments using a low-cost GNSS receiver experiment in two complex urban canyons in Hong Kong achieve the mean horizontal positioning error of the real-time results of 1.606 m and 4.055 m, respectively. Compared with the existing classic PPP-RTK methods based on Kalman filters, the proposed method improves the availability of the positioning result. It mitigates in part large positioning errors, ensuring that the maximum horizontal positioning error is less than 10 m in complex urban environments.