Detection of GNSS NLOS signals in urban environments via stacking ensemble learning and signal features

作者：Hua Chen, Li Liu, Zhao Li, Weiping Jiang, Ran Lu, Jian Wang, Zongkun Zhou · 发表于：Geo-spatial Information Science · 年份：2025 · DOI：10.1080/10095020.2025.2568124 · 被引用次数：3 · 研究领域：GNSS positioning and interference、Target Tracking and Data Fusion in Sensor Networks、Indoor and Outdoor Localization Technologies

In urban environments, Global Navigation Satellite System (GNSS) signals are highly susceptible to obstructions from tall buildings, leading to Non-Line-of-Sight (NLOS) errors, and severe positioning degradation. Machine Learning (ML)-based NLOS detection has gained significant attention, due to its high accuracy and the advantage of requiring no hardware modifications. However, the existing studies predominantly rely on single-model architectures, which often suffer from poor generalization, and a tendency to converge to local optima. To overcome these limitations, this study proposes a GNSS NLOS detection method based on a two-layer Stacking Ensemble Learning (SEL) model and five key GNSS signal features. A comprehensive weighting model is then applied to correct NLOS-induced pseudorange errors. Results show that the SEL model, utilizing our selected GNSS signal feature set for NLOS detection, achieves a 13.1% improvement in classification accuracy, compared with the traditional feature sets. This improvement is primarily attributed to the more precise and comprehensive selection of features, which collectively consider satellite geometry, signal strength variations, dynamic characteristics, and pseudorange errors. Moreover, the SEL model integrates multiple heterogeneous base models, demonstrating superior generalization capability and higher detection accuracy, making it particularly well suited for GNSS observation data processing in complex urban areas, encompassing mul...