  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$\sigma$-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.
