Ship Motion Dynamics from AIS Trajectories Using Fractal Micromovement Representation Framework

Accurate characterization of local ship motion remains challenging because conventional AIS trajectory analysis primarily relies on global or averaged motion descriptors that overlook short-term navigation dynamics. This research introduces a fractal micromovement representation framework that identifies localized motion episodes and transforms them into symbolic descriptors suitable for structural trajectory analysis. The framework combines sliding-window Higuchi fractal analysis, quantile-based episode detection, temporal aggregation, and symbolic encoding of the resulting micromovement episodes. To formalize local motion dynamics, each micro-episode is transformed into a symbolic fractal code that reflects the topological structure of the geometric complexity of the vessel’s trajectory. Application of the proposed framework to experimental trajectory data demonstrates its ability to localize intervals of elevated trajectory complexity and transform them into compact symbolic representations suitable for structural comparison. Quantitative sensitivity analysis and comparison with an independent kinematic reference further characterize the stability and operational correspondence of the fractal representation. The framework is intended for retrospective trajectory analysis, while behavioral recognition, anomaly detection, forecasting, and integration with higher-precision navigation data are considered prospective applications.