GNSS Signal Characteristics Analysis in Different Water Layers and Navigation Context Clustering

Objective Navigation context comprises observation environment and carrier activity. While global navigation satellite system (GNSS) can provide high-precision navigation, positioning, and timing services for users, users can also annotate the types of navigation scenarios they are in by distinguishing the quality and features of terminal observation values. Navigation context clustering is important for adaptive seamless navigation and location services, which can significantly promote context adaptability, configuration flexibility, and system robustness.

Methods In terms of current issues that the division of water/underwater context is simple and has insufficient granularity, the quality and characteristics of GNSS observation signals in different water depth layers are analyzed by collecting GNSS observations in different water regions.

Results and Conclusions The results show that the underwater navigation context has remarkable layering. Considering that the navigation context has inherent properties such as segmentation, merging, and connectivity, this paper subdivides the underwater navigation context into three layers: Submerged water (about less than 6.5 cm), shallow water (about greater than 6.5 cm and less than 8.5 cm), and deep water (about greater than 8.5 cm) through K-means clustering, and conducts navigation context classification experiments using various GNSS signal features. It reveals that the clustering accuracy rate is 90.4%, indicating the effectiveness of submerged-shallow-deep navigation context division.