Ship Trajectory Prediction Model for Space-Based Maritime Surveillance

This research proposes an anomaly detection workflow intended primarily for integration into satellite-based tip and cue services for maritime surveillance. The workflow is centred around a ship trajectory prediction model which is designed to respond to anomalous events such as AIS "shut-off" events. This is important for accurately predicting the trajectories of potentially suspicious vessels that are moving, and subsequently scheduling the tasking of satellite acquisitions to monitor these vessels. The research implements a ship trajectory prediction model based on AIS data using Recurrent Neural Networks (RNNs). The efficacy of the model is evaluated based on the mean great circle distance between predicted and actual vessel trajectories, demonstrating satisfactory performance for typical motion patterns while acknowledging certain limitations in prediction accuracy. Overall, this study represents a significant step forward in the integration of imaging satellites and AIS data for maritime surveillance, offering a promising approach for anomaly detection and improving the efficiency of satellite-based monitoring systems. A GitHub repository containing the source code and related materials for this work is made available.