T-SPP: Improving GNSS Single-Point Positioning Performance Using Transformer-Based Correction

doi:10.1155/2024/6643723

	T-SPP: Improving GNSS Single-Point Positioning Performance Using Transformer-Based Correction
	Wu, Fan 1; Wei, Liangrui 1; Luo, Haiyong 2; Zhao, Fang 1; Ma, Xin 1; Ning, Bokun 1
	2024-02-29
发表期刊	INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
ISSN	0884-8173
卷号	2024 页码:15
摘要	GNSS (global navigation satellite systems) technology enables high-precision single-point positioning (SPP) in open environments. However, the accuracy of GNSS positioning is significantly compromised in complex urban canyons due to signal obstructions and non-line-of-sight propagation errors. To address this challenge, we propose a GNSS displacement estimation algorithm. This method learns nonlinear dependencies between GNSS raw measurements and corresponding position changes, capturing dynamic and layered features in GNSS measurement data for displacement estimation. We introduce a denoising auto-encoder (DAE) to preprocess raw GNSS observations, reducing the impact of noise. The model simultaneously outputs estimated displacement and model confidence. The fusion process dynamically combines positioning results from the SPP algorithm and the D-Tran model, adaptively blending them to achieve accurate and optimal positioning estimation. This approach optimizes the accuracy of estimated positioning results while maintaining confidence in the estimation. Experimental results show a 61% reduction in root mean square error (RMSE) and 100% availability in urban canyon environments compared to traditional single-point positioning techniques.
DOI	10.1155/2024/6643723
收录类别	SCI
语种	英语
资助项目	National Basic Research Program of China (973 Program)[2022YFB3904700] ; National Key Research and Development Program[62261042] ; National Key Research and Development Program[62002026] ; National Natural Science Foundation of China ; Key Research Projects of the Joint Research Fund for Beijing Natural Science Foundation[L221003] ; Fengtai Rail Transit Frontier Research Joint Fund[4212024] ; Fengtai Rail Transit Frontier Research Joint Fund[4222034] ; Beijing Natural Science Foundation[XDA28040500] ; Strategic Priority Research Program of Chinese Academy of Sciences[2022RC13] ; Fundamental Research Funds for the Central Universities ; BUPT Excellent Ph.D.[CX2022131] ; Students Foundation
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001180440700001
出版者	WILEY-HINDAWI
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38791
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Luo, Haiyong
作者单位	1.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing, Peoples R China 2.Chinese Acad Sci, Res Ctr Ubiquitous Comp Syst, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wu, Fan,Wei, Liangrui,Luo, Haiyong,et al. T-SPP: Improving GNSS Single-Point Positioning Performance Using Transformer-Based Correction[J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS,2024,2024:15.
APA	Wu, Fan,Wei, Liangrui,Luo, Haiyong,Zhao, Fang,Ma, Xin,&Ning, Bokun.(2024).T-SPP: Improving GNSS Single-Point Positioning Performance Using Transformer-Based Correction.INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS,2024,15.
MLA	Wu, Fan,et al."T-SPP: Improving GNSS Single-Point Positioning Performance Using Transformer-Based Correction".INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2024(2024):15.