Toward Wide-Area and High-Precision Positioning With LEO Constellation Augmented PPP-RTK

The precise point positioning-real-time kinematic (PPP-RTK) method, which could achieve instantaneous ambiguity resolution (AR) with the regional atmospheric corrections, has been recognized as a preferred alternative for emerging applications such as self-driving cars, unmanned aerial vehicles, and mobile robots. Nevertheless, the performance of PPP-RTK is limited by the distribution and interstation distance of regional networks. Presently, the rapid development of the low Earth orbit (LEO) constellation brings innovative opportunities for satellite navigation. The fusion of LEO and global navigation satellite system (GNSS), which brings significant improvement in satellite visibility and spatial geometry, is expected to further enhance the performance of PPP-RTK and finally provide a wide-area and high-precision positioning service. In this study, we propose a novel LEO-augmented PPP-RTK model that leverages massive LEO observations and atmospheric augmented information. The spatial–temporal characteristics of uncalibrated phase delays (UPDs) and network-derived atmospheric corrections for fast-moving LEO satellites are analyzed, and their effectiveness on rapid AR is demonstrated. The positioning performance is comprehensively investigated with multiscale regional networks. The results indicate that the positioning accuracy of the traditional PPP-RTK can be improved by 15%–27% and 20%–40% with the augmentation of 144-LEO and 360-LEO constellations, respectively. Moreover, by incorporating the 360-LEO constellation, the time required for the first fix can be reduced to less than 1 and 10 s for network spacings of 400 and 800 km, respectively, exhibiting a significant improvement of 88%–92% compared with the traditional PPP-RTK method. These findings demonstrate the potential of LEO-augmented PPP-RTK to provide rapid (even instantaneous) centimeter-accurate positioning for an unlimited number of users over a large coverage area.