Improving Maritime Security via Automatic Navigational Monitoring

Abstract

Uninterrupted monitoring of illegal activities occurring in a large maritime area with numerous vessels is very challenging. This article reports the development of system for automated monitoring of navigational data obtained via the automatic identification system (AIS). The AIS information obtained from a vessel is used to build up a time series dataset where an autoregression integrated moving average (ARIMA) model is used on the dataset to predict the status and future position of the vessel. Since the actual navigational trajectories of vessels are predictable, the projected information obtained from the ARIMA model can be compared against the next actual AIS information. The model could decide to trigger a warning alert using preset criteria after comparing the prediction to the actual data. This article shows two cases where a particular ship displayed suspicious behaviours, prompting the model to trigger a warning. While the preset criteria can initially be decided by the user, such criteria can be shaped or trained ‘on the fly’ to produce more accurate decisions as more similar cases are detected. The author believes that the ARIMA model is simple and robust for monitoring suspicious behaviours. It is versatile and warning criteria can be defined and shaped according to user requirements.