文献类型：期刊文献

英文题名：Feature Adaptive Iteration and Multipath Pseudorange Correction for GNSS Positioning in Urban Environments

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

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

年份：2025

卷号：12

期号：24

起止页码：54420

外文期刊名：IEEE INTERNET OF THINGS JOURNAL

收录：;EI(收录号：20254419401317);WOS:【SCI-EXPANDED(收录号:WOS:001635904800006)】；

基金：This work was supported by the National Natural Science Foundation of China under Grant 62231010 and Grant 61971278.

语种：英文

外文关键词：Satellites; Global navigation satellite system; Receivers; Prediction algorithms; Accuracy; Adaptation models; Urban areas; Predictive models; Machine learning algorithms; Classification algorithms; Global Navigation Satellite System (GNSS); machine learning; multipath (MP); non-line-of-sight (NLOS); pseudorange error correction

摘要：The Global Navigation Satellite System (GNSS) has established strong connections with users worldwide. However, when propagating in complex urban environments, GNSS signals are susceptible to blockage and reflection from surrounding obstacles, resulting in multipath (MP) effects and non-line-of-sight (NLOS) reception, significantly degrading positioning precision. With the rapid development of artificial intelligence technology, numerous researchers have advocated employing machine learning algorithms to address this issue. To effectively mitigate MP and NLOS, this study first introduces a novel signal classification-based MP pseudorange error correction model by initially categorizing signals into line-of-sight (LOS), MP, and NLOS. Second, to enhance the prediction accuracy of the model, an innovative feature adaptive iteration algorithm is proposed to update feature values. Additionally, this study is the first to propose utilizing the more reliable differential receiver clock error nonexcluded pseudorange error (DNEPE), obtained through differential techniques, as the label for pseudorange error prediction. The performance of the algorithm model was tested using dynamic GNSS raw data collected by the Google team in the San Francisco area of the USA. The experimental results demonstrate that the proposed method in this study significantly improves positioning precision compared to baseline and comparative methods. Specifically, in comparison to the baseline, the proposed method exhibits an enhancement of 54.50% and 66.10% in 2-D root-mean-square error (RMSE) of positioning errors on the testing sets 1 and 2, respectively. Overall, the methods proposed in this study offer novel insights and robust support for the mitigation of MP and NLOS in urban environments.