Research on Ship Trajectory under Multi source Spatiotemporal Data

To explore the application of multi-source spatiotemporal data fusion technology in ship trajectory analysis, this study develops a fusion algorithm that integrates Kalman filtering and Bayesian estimation. Through the fusion of multi-source data, the positional accuracy of the trajectories is significantly improved, with experimental results showing that the average error is reduced from 58 meters to 23 meters. The results demonstrate that the proposed algorithm effectively enhances the accuracy and reliability of vessel monitoring, which is of great significance for maritime traffic safety.