Hui Luo

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.

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.

In urban canyon areas, the receiver positioning error of the Global Navigation Satellite System (GNSS) is mainly affected by multipath (MP) effects and non-line-of-sight (NLOS) reception, especially NLOS reception issues, which sometimes even cause errors of hundreds of meters. Therefore, detecting and suppressing NLOS reception has become an effective means to improve the positioning accuracy of GNSS, which has been extensively researched and used in the literature. Based on the preferred combination of satellite signal features, a LOS/NLOS signal classification method utilizing the Light Gradient Boosting Machine (LGBM) is first proposed in this paper. And based on this, while calculating the position coordinates by combining the weighted least squares (WLS) algorithm and the particle swarm optimization (PSO) algorithm, the reception of NLOS can be suppressed so that its influence can be significantly reduced, and finally, a novel PSO-LGBM-WLS algorithm model is established in this study. In addition, in view of the difficulty of obtaining labels in NLOS detection using machine learning-based algorithms, we also propose to use the pseudorange error as the basis for label production and use PSO to optimize the classification threshold of pseudorange error, which also helps to reduce the final positioning error from another aspect. In the real experiments, relying on the data collected by the Google team in the San Francisco area, the proposed algorithm model is tested in terms of LOS/NLOS signal classification and positioning accuracy, which are compared with other machine learning models and positioning methods. The experimental results show that the proposed algorithm has the advantages of high classification accuracy and short consumption time, which provides the possibility for practical application. From the positioning results, the proposed PSO-LGBM-WLS algorithm model shows significant effects on improving positioning accuracy by detecting and suppressing NLOS reception.