A spatiotemporal clustering approach to maritime domain awareness

Abstract

Spatiotemporal clustering is the process of grouping objects based on both their spatial and temporal similarity. This approach is useful when considering the distance between objects and how that distance changes through time. Spatiotemporal clustering analysis is applied to the maritime domain in this thesis, specifically to a defined area of water, during a period of time, in order to gain behavioral knowledge of vessel interactions and provide the opportunity to screen such interactions for further investigation. The proposed spatiotemporal clustering algorithm spatially clusters vessels in the water space using k-means clustering analysis, kinematically refines the clusters based on the similarity of vessel headings, speeds and the distance between them, and temporally analyzes the continuity of membership of the kinematic clusters through time to determine which clusters are moving. The algorithm is implemented in the MATLAB programming environment, verified with a synthetic data scenario, and validated with two real-world datasets.