  In urban areas, dense buildings frequently block and reflect global
positioning system (GPS) signals, resulting in the reception of a few visible
satellites with many multipath signals. This is a significant problem that
results in unreliable positioning in urban areas. If a signal reception
condition from a certain satellite can be detected, the positioning performance
can be improved by excluding or de-weighting the multipath contaminated
satellite signal. Thus, we developed a machine-learning-based method of
classifying GPS signal reception conditions using a dual-polarized antenna. We
employed a decision tree algorithm for classification using three features, one
of which can be obtained only from a dual-polarized antenna. A machine-learning
model was trained using GPS signals collected from various locations. When the
features extracted from the GPS raw signal are input, the generated
machine-learning model outputs one of the three signal reception conditions:
non-line-of-sight (NLOS) only, line-of-sight (LOS) only, or LOS+NLOS. Multiple
testing datasets were used to analyze the classification accuracy, which was
then compared with an existing method using dual single-polarized antennas.
Consequently, when the testing dataset was collected at different locations
from the training dataset, a classification accuracy of 64.47% was obtained,
which was slightly higher than the accuracy of the existing method using dual
single-polarized antennas. Therefore, the dual-polarized antenna solution is
more beneficial than the dual single-polarized antenna solution because it has
a more compact form factor and its performance is similar to that of the other
solution.
