A Spatio-temporal Track Association Algorithm Based on Marine Vessel Automatic Identification System Data

TL;DR

This paper presents a novel spatio-temporal data association algorithm for tracking maritime vessels using Automatic Identification System data, addressing real-world challenges like unknown vessel counts and data gaps in a threat environment.

Contribution

The work introduces a new data association algorithm tailored for maritime vessel tracking with anonymized data and simulated operational complexities.

Findings

The algorithm performs competitively on multiple test sets.

It effectively handles data gaps and unknown vessel identities.

Provides a robust solution for real-time maritime surveillance.

Abstract

Tracking multiple moving objects in real-time in a dynamic threat environment is an important element in national security and surveillance system. It helps pinpoint and distinguish potential candidates posing threats from other normal objects and monitor the anomalous trajectories until intervention. To locate the anomalous pattern of movements, one needs to have an accurate data association algorithm that can associate the sequential observations of locations and motion with the underlying moving objects, and therefore, build the trajectories of the objects as the objects are moving. In this work, we develop a spatio-temporal approach for tracking maritime vessels as the vessel's location and motion observations are collected by an Automatic Identification System. The proposed approach is developed as an effort to address a data association challenge in which the number of vessels as well as the vessel identification are purposely withheld and time gaps are created in the datasets to mimic the real-life operational complexities under a threat environment. Three training datasets and five test sets are provided in the challenge and a set of quantitative performance metrics is devised by the data challenge organizer for evaluating and comparing resulting methods developed by participants. When our proposed track association algorithm is applied to the five test sets, the algorithm scores a very competitive performance.