Improving Vertical Positioning Accuracy with the Weighted Multinomial Logistic Regression Classifier
This work addresses incremental improvements in vertical positioning accuracy for GPS-based applications, such as navigation or location services.
The paper tackled improving vertical positioning accuracy using GPS and barometric data, achieving an accuracy of 5 meters with the Weighted Multinomial Logistic Regression method, compared to 5.9 meters and 5.4 meters with baseline methods for 67% of test points.
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.