An Analytic Framework for Maritime Situation Analysis

TL;DR

This paper introduces a probabilistic machine learning framework using Hidden Markov Models and Support Vector Machines for analyzing maritime traffic data to detect anomalous vessel interactions and improve security monitoring.

Contribution

It presents a novel analytic framework combining probabilistic modeling and machine learning for maritime situation analysis, enhancing anomaly detection capabilities.

Findings

Effective classification of vessel interaction patterns

Improved detection of maritime anomalies

Utilization of Hidden Markov Models and SVMs

Abstract

Maritime domain awareness is critical for protecting sea lanes, ports, harbors, offshore structures and critical infrastructures against common threats and illegal activities. Limited surveillance resources constrain maritime domain awareness and compromise full security coverage at all times. This situation calls for innovative intelligent systems for interactive situation analysis to assist marine authorities and security personal in their routine surveillance operations. In this article, we propose a novel situation analysis framework to analyze marine traffic data and differentiate various scenarios of vessel engagement for the purpose of detecting anomalies of interest for marine vessels that operate over some period of time in relative proximity to each other. The proposed framework views vessel behavior as probabilistic processes and uses machine learning to model common vessel interaction patterns. We represent patterns of interest as left-to-right Hidden Markov Models and classify such patterns using Support Vector Machines.