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

IF 9 1区 地球科学 Q1 ENGINEERING, AEROSPACE Satellite Navigation Pub Date : 2024-05-13 DOI:10.1186/s43020-024-00135-8
Hongjin Xu, Xingyu Chen, Jikun Ou, Yunbin Yuan
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Abstract

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.
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众包 RTK:基于海量车辆 GNSS 数据构建空间高分辨率大气地图的新型 GNSS 定位框架
高质量的空间大气延迟校正信息对于在精确定位中实现快速整数模糊分辨率(AR)至关重要。然而,传统的实时精确定位框架(即 NRTK 和 PPP-RTK)依赖于通过昂贵且分布稀疏的 CORS 网络进行空间低分辨率大气延迟校正。这导致对公众的吸引力有限。随着自动驾驶汽车的大规模生产,可以探索更具成本效益和更广泛的数据源,以创建空间高分辨率大气地图。在本研究中,我们提出了一种新的 GNSS 定位框架,它依赖于双基站、海量车辆 GNSS 数据和众包大气延迟校正地图(CAM)。该地图可由配备 GNSS 接收器的车辆以众包方式轻松生成和更新。具体来说,该地图由站间单差分电离层和对流层延迟组成。我们介绍了单个车辆的 CAM 初始化、云上 CAM 维护和 CAM 增强用户端定位的整个框架。地图数据在车辆中收集和预处理。然后,将众包数据上传到云服务器。来自多辆车的海量数据在云端合并,及时更新 CAM。最后,CAM 将增强用户定位性能。这个框架形成了一个有益的循环,CAM 的空间分辨率和用户定位性能相互提高。我们在实际实验中验证了所建议框架的性能以及在不同空间尺度上的应用效力。我们强调,该框架是一种可靠而实用的定位解决方案,可满足无处不在的高精度定位要求。
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来源期刊
CiteScore
19.40
自引率
6.20%
发文量
25
审稿时长
12 weeks
期刊介绍: Satellite Navigation is dedicated to presenting innovative ideas, new findings, and advancements in the theoretical techniques and applications of satellite navigation. The journal actively invites original articles, reviews, and commentaries to contribute to the exploration and dissemination of knowledge in this field.
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