Improving Positional Accuracy Using Relative Measurement between Android Smartphones

In this study, we propose a relative and clustering analysis correction (RCC) technique capable of improving the location accuracy of a smartphone global navigation satellite system (GNSS). The RCC technique improves the accuracy of the Android GNSS by eliminating common error components from pseudoscope measurements as well as noncommon errors through cluster analysis using Android GNSS signal attributes. Cluster analysis was applied to the RCC technique using the optimal clustering method among the hierarchical clustering, K-means clustering, and neural network clustering methods. As a result of verifying the RCC technique, the following results were obtained. The distance error of a zero-baseline experiment, which was performed to check the relative accuracy and precision between smartphone GNSSs, was 0.572 m for two sessions, which showed that the noise-causing error of the Android smartphone GNSS used in the experiment occurred similarly in each session. Positioning accuracy was much lower in a multipath environment than in an open environment due to the reflection and refraction of satellite signals by obstacles, such as buildings around the receiver and multipath generation due to low-elevation non-line-of-sight satellite signals. However, observations confirmed that applying the RCC technology to the Android smartphone GNSS with errors of more than 5 m in multipath environments can secure high location accuracy, even in multipath environments.