Maritime Vessel Tracking

TL;DR

This paper introduces a hybrid method combining physics-based models and neural classifiers to improve vessel track relabeling in large-scale, heterogeneous AIS data, enhancing accuracy in maritime traffic monitoring.

Contribution

It presents a novel hybrid pipeline that effectively relabels vessel trajectories in challenging, large-scale AIS datasets by integrating physics-based screening with supervised learning.

Findings

Improved position accuracy over unsupervised methods.

Effective scaling to high-volume, diverse AIS streams.

Demonstrated robustness across different operating environments.

Abstract

The Automatic Identification System (AIS) provides time stamped vessel positions and kinematic reports that enable maritime authorities to monitor traffic. We consider the problem of relabeling AIS trajectories when vessel identifiers are missing, focusing on a challenging nationwide setting in which tracks are heavily downsampled and span diverse operating environments across continental U.S. waters. We propose a hybrid pipeline that first applies a physics-based screening step to project active track endpoints forward in time and select a small set of plausible ancestors for each new observation. A supervised neural classifier then chooses among these candidates, or initiates a new track, using engineered space time and kinematic consistency features. On held out data, this approach improves posit accuracy relative to unsupervised baselines, demonstrating that combining simple motion models with learned disambiguation can scale vessel relabeling to heterogeneous, high volume AIS streams.