Crowdsourcing RTK: a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

• Published in: Satellite navigation 2024-12, Vol.5 (1), p.13-18, Article 13
• Main Authors: Xu, Hongjin, Chen, Xingyu, Ou, Jikun, Yuan, Yunbin
• Format: Article
• Language: English
• Subjects: Accuracy; Ambiguity; Collaboration; Costs; Crowdsourced atmospheric delay correction maps; Crowdsourced ionosphere; Crowdsourced troposphere; Crowdsourcing; Engineering; GNSS Ionospheric Monitoring and Modelling; Ionosphere; New GNSS positioning framework; Original Article; Quality control; Satellites; Spatial high-resolution atmospheric delay correction; Stochastic models; Ubiquitous; Vehicles
• ISSN: 2662-9291; 2662-1363
• DOI: 10.1186/s43020-024-00135-8

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution (AR) in precise positioning. However, traditional real-time precise positioning frameworks (i.e., NRTK and PPP-RTK) depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network. This results in limited public appeal. With the mass production of autonomous driving vehicles, more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps. In this study, we propose a new GNSS positioning framework that relies on dual base stations, massive vehicle GNSS data, and crowdsourced atmospheric delay correction maps (CAM). The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way. Specifically, the map consists of between-station single-differenced ionospheric and tropospheric delays. We introduce the whole framework of CAM initialization for individual vehicles, on-cloud CAM maintenance, and CAM-augmented user-end positioning. The map data are collected and preprocessed in vehicles. Then, the crowdsourced data are uploaded to a cloud server. The massive data from multiple vehicles are merged in the cloud to update the CAM in time. Finally, the CAM will augment the user positioning performance. This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other. We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales. We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.