Stacking Ensemble Learning for Non-Line-of-Sight Detection of Global Navigation Satellite System

While the global navigation satellite system (GNSS) has been widely used to provide high-precision location services in many applications, it usually suffers from performance degradation due to non-line-of-sight (NLOS) reception. As the received NLOS signals might have great measurement errors especially in urban canyons, they should be detected to mitigate the errors contaminating the positioning systems. However, the NLOS detection is quite challenging as the accuracy rate is usually highly related to the surrounding environment the receiver is located in. To address this problem, we propose a stacking ensemble learning (SEL) method for the NLOS detection of GNSS. First, satellite measurement features are extracted from the GNSS raw measurements via a designed data processing module. Then, they are input to the SEL module consisting of two levels of machine learning models. In the first level, a support vector machine (SVM) and an extreme gradient boosting (XGBoost) are adopted in parallel, and the outputs of the fist-level models are input to the second-level logistic regression (LR) to obtain NLOS predictions. The proposed SEL module combines the views of different models to the measurement features to address the shortcomings of each single model and improve the model’s generalization. Experimental results on real GNSS observations in urban canyons show that the proposed method outperforms the baseline machine learning methods with obvious detection accuracy improvements.