Enhancing PPP-RTK performance in sparse reference networks and complex terrain via Pangu-Weather Model and GNSS integration
TL;DR
A novel tropospheric delay modeling method that integrates tropospheric delays derived from the Pangu-Weather Model with Global Navigation Satellite System (GNSS) observations to enhance PPP-RTK positioning performance and demonstrates the effectiveness, robustness, and applicability of the proposed method.
Abstract
Tropospheric delay modeling accuracy is a critical factor affecting the performance of Precise Point Positioning Real-Time Kinematic (PPP-RTK). However, reliable tropospheric delay estimation remains challenging in regions characterized by sparse reference stations and complex terrain. To address this issue, this study proposes a novel tropospheric delay modeling method that integrates tropospheric delays derived from the Pangu-Weather Model with Global Navigation Satellite System (GNSS) observations to enhance PPP-RTK positioning performance. The proposed method first estimates tropospheric delays from both techniques at different locations and subsequently models the inter-technique bias. The method is validated using a network of 274 GNSS stations across Europe. Results from a one-month experiment demonstrate that the Pangu-Weather Model achieves zenith wet delay (ZWD) estimation accuracy at the level of approximately 2–3 cm. In regions with sparse reference stations and complex terrain, the proposed method improves ZWD accuracy, with root mean square reductions of 36.1%, 54.7%, and 68.3% compared with three conventional methods. In PPP-RTK positioning, the proposed method enhances convergence speed, accuracy, and stability. The average convergence time is reduced by 9.9%, 12.7%, and 11.3%, while the three-dimensional positioning accuracy is improved by up to 12.6%, 27.8%, and 22.8%. The proportion of vertical positioning errors within 5 cm increases from 95.6% to 99.4%. Furthermore, large-scale experiments demonstrate consistent improvements across multiple error intervals, with the proportion of errors within 1 cm increased by up to 3.2%, 3.8%, and 16.5% in the east, north, and up components, respectively. These results demonstrate the effectiveness, robustness, and applicability of the proposed method, particularly in challenging environments with sparse reference stations and complex terrain.