Feature Adaptive Iteration and Multipath Pseudorange Correction for GNSS Positioning in Urban Environments

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 firstly introduces a novel signal classification-based MP pseudorange error correction model by initially categorizing signals into line-of-sight (LOS), MP, and NLOS. Secondly, 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 non-excluded 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 United States. 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 two-dimensional 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.