论文标题
通过加权多项式逻辑回归分类器提高垂直定位精度
Improving Vertical Positioning Accuracy with the Weighted Multinomial Logistic Regression Classifier
论文作者
论文摘要
在本文中，提出了一种通过全局定位系统（GPS）信息和气压压力值提高垂直定位精度的方法。首先，我们清除了在各种环境中收集的原始数据的空值，并使用3 $σ$ rule来识别异常值。其次，对加权的多项式逻辑回归（WMLR）分类器进行训练以获得异常值的预测高度。最后，为了验证其效果，我们比较了已清洁数据集的MLR方法，WMLR方法和支持向量机（SVM）方法，该方法被认为是测试基线。数值结果表明，垂直定位精度从5.9米（MLR方法），5.4米（SVM方法）提高到5米（WMLR方法）的67％测试点。
In this paper, a method of improving vertical positioning accuracy with the Global Positioning System (GPS) information and barometric pressure values is proposed. Firstly, we clear null values for the raw data collected in various environments, and use the 3$σ$-rule to identify outliers. Secondly, the Weighted Multinomial Logistic Regression (WMLR) classifier is trained to obtain the predicted altitude of outliers. Finally, in order to verify its effect, we compare the MLR method, the WMLR method, and the Support Vector Machine (SVM) method for the cleaned dataset which is regarded as the test baseline. The numerical results show that the vertical positioning accuracy is improved from 5.9 meters (the MLR method), 5.4 meters (the SVM method) to 5 meters (the WMLR method) for 67% test points.
