这项研究解决了城市环境中GNSS定位精度差的痛点，做导航定位、自动驾驶或智能交通的开发者可以直接参考其激活函数加权方法，值得关注。
该研究提出一种基于激活函数的机器学习框架，用于改进加权最小二乘（WLS）GNSS定位算法。在城市峡谷等复杂环境中，多径效应和非视距信号会导致定位误差，该框架通过集成学习算法评估信号质量，并利用激活函数（如sigmoid）将预测分数转化为权重，从而提升定位精度。在香港和东京的真实数据集测试中，sigmoid函数在不同算法和星座配置下均表现最佳，显著降低了单星座和多星座场景的定位误差。该方法还展现出良好的地理迁移性，在类似城市化程度的区域训练后性能保持稳定。
A Machine Learning Framework for Weighted Least Squares GNSS Positioning based on Activation Functions
Global Navigation Satellite Systems (GNSS) are widely used to provide position, velocity, and timing (PVT) information for various applications, including transportation, location-based communication services, and intelligent agriculture. In urban canyons, high-rise buildings and narrow streets can cause signal obstruction, non-line-of-sight (NLOS) reception, and multipath effects that introduce errors in GNSS pseudorange measurements. Although multi-constellations GNSS effectively increase the number of available satellites, the inclusion of degraded signals can lead to severe positioning errors. This study proposes a machine learning framework for the weighted least squares (WLS) algorithm incorporating activation functions to enhance positioning accuracy. Several signal quality indicators are employed as training features for ensemble learning algorithms to identify poor quality signals by providing quality scores. Then, activation functions are employed to transform the machine learning predicted scores to appropriate weights for WLS positioning. To evaluate the performance of our approach, experiments are conducted using real-world datasets from Hong Kong and Tokyo urban areas. Comparative analysis of activation functions reveals that sigmoid functions consistently yield the greatest improvements with different machine learning algorithms and GNSS constellation configurations. The proposed algorithm demonstrates substantial reductions in positioning errors for both single- and multiconstellation scenarios. Furthermore, our results indicate that the proposed algorithm exhibits strong geographical transferability. The proposed algorithm maintains comparable level of performance when trained on data from other regions with similar levels of urbanization.