文献类型：期刊文献

英文题名：Novel Robust Labeling Methods Applied to GNSS Signal Classification Based on Deviation and Differential

作者：Luo, Hui[1];Liu, Ji[2];Yao, Bing[2];He, Di[1];Yu, Wenxian[3]

机构：[1]Shanghai Jiao Tong Univ, Sch Sensing Sci & Engn, Sch Elect Informat & Elect Engn, Shanghai Key Lab Nav & Locat Based Serv, Shanghai 200240, Peoples R China;[2]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[3]Shanghai Jiao Tong Univ, Sch Sensing Sci & Engn, Sch Elect Informat & Elect Engn, Shanghai Key Lab Intelligent Sensing & Recognit, Shanghai 200240, Peoples R China

年份：2025

卷号：74

期号：11

起止页码：17097

外文期刊名：IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

收录：;EI(收录号：20252418612311);WOS:【SCI-EXPANDED(收录号:WOS:001621303400034)】；

基金：Thiswork was supported by the National Natural Science Foundation of China underGrant 62231010 and Grant 61971278.

语种：英文

外文关键词：Receivers; Clocks; Global navigation satellite system; Satellites; Labeling; Nonlinear optics; Pattern classification; Delays; Accuracy; Three-dimensional displays; Global navigation satellite system (GNSS); non-line-of-sight (NLOS); multipath; signal classification; labeling

摘要：Global navigation satellite system (GNSS) signals are highly susceptible to surrounding conditions in complex urban environments, resulting in multipath (MP) effects and non-line-of-sight (NLOS) reception. These phenomena significantly compromise positioning precision in GNSS-based applications. Consequently, the detection and suppression of MP and NLOS have become the focus of research. This study delves into the issue of signal type labeling in the emerging research area of utilizing supervised learning for GNSS signal classification. Two novel robust labeling methods are proposed, namely receiver clock error non-excluded pseudorange error deviation (NEPED) and differential receiver clock error non-excluded pseudorange error (DNEPE). In the experimental validation process, we conducted tests on the accuracy and robustness of labels based on dynamic GNSS data collected by the Google team in the San Francisco Bay Area, and comparative analyses were performed with three other methods. The experimental results indicate that both NEPED and DNEPE exhibit superior accuracy and robustness. In particular, DNEPE consistently demonstrates much better performance across all experiments in comparison to the alternatives and is a more recommended method.