论文标题
使用双天线的基于机器学习的GPS多径检测方法
Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas
论文作者
论文摘要
在城市地区，全球导航卫星系统（GNSS）信号通常会被建筑物反映或阻塞，从而导致较大的位置错误。在这项研究中，我们提出了一种使用双天线的全球定位系统（GPS）多径检测的机器学习方法。可以对GPS信号接收条件进行分类的机器学习模型进行了训练，该模型已通过建议的特征选择了多个GPS测量。我们在应用四种机器学习算法后，我们应用了五个功能，包括从双天线获得的功能，并评估了模型的分类性能：梯度增强决策树（GBDT）（GBDT），随机森林，决策树和K-Neareart邻居（KNN）。发现当测试数据集与培训数据集相同的位置收集时，分类精度达到了82％-96％。但是，当在与培训数据不同的位置收集测试数据集时，获得了44％-77％的分类精度。
In urban areas, global navigation satellite system (GNSS) signals are often reflected or blocked by buildings, thus resulting in large positioning errors. In this study, we proposed a machine learning approach for global positioning system (GPS) multipath detection that uses dual antennas. A machine learning model that could classify GPS signal reception conditions was trained with several GPS measurements selected as suggested features. We applied five features for machine learning, including a feature obtained from the dual antennas, and evaluated the classification performance of the model, after applying four machine learning algorithms: gradient boosting decision tree (GBDT), random forest, decision tree, and K-nearest neighbor (KNN). It was found that a classification accuracy of 82%-96% was achieved when the test data set was collected at the same locations as those of the training data set. However, when the test data set was collected at locations different from those of the training data, a classification accuracy of 44%-77% was obtained.
