Belief modeling for maritime surveillance

In maritime surveillance, the volume of information to be processed is very large and there is a great deal of uncertainty about the data. There are many vessels at sea at every point in time, and the vast majority of them pose no threat to security. Sifting through all of the benign activity to find unusual activities is a difficult problem. The problem is made even more difficult by the fact that the available data about vessel activities is both incomplete and inconsistent. In order to manage this uncertainty, automated anomaly detection software can be very useful in the early detection of threats to security. This paper introduces a high-level architecture for an anomaly detection system based on a formal model of beliefs with respect to each entity in some domain of interest. In this framework, the system has beliefs about the intentions of each vessel in the maritime domain. If the vessel behaves in an unexpected manner, these intentions are revised and a human operations centre worker is notified. This approach is flexible, scalable, and easily manages inconsistent information. Moreover, the approach has the pragmatic advantage that it uses expert information to inform decision making, but the required information is easily obtained through simple ranking exercises.

1 Introduction

Global maritime surveillance involves the monitoring of several hundred vessels in many cases. Using existing sensors, it is possible to monitor an individual ship very effectively if we are aware that it may pose a threat to security. However, due to the volume of information with which we are faced, it is often difficult for a human observer to determine which ships should be subjected to detailed monitoring. As such, the study of automated anomaly detection systems has emerged as an important topic in maritime surveillance. In this paper, we present an approach to anomaly detection based on a formal model of belief change that has been developed in the Artificial Intelligence community. We present our approach as a high-level architecture, built to complement an existing rule-based expert system for anomaly detection [13].