Multi model LSTM architecture for Track Association based on Automatic Identification System Data

TL;DR

This paper presents a novel multi-model LSTM framework that leverages AIS data and geodesic distance metrics to improve vessel track association accuracy in marine surveillance.

Contribution

It introduces a deep learning-based approach using LSTM networks combined with geodesic distance for effective vessel track association from AIS data.

Findings

Achieved high precision, recall, and F1 scores in vessel track association.

Demonstrated the effectiveness of LSTM in processing multivariate temporal AIS data.

Provided a comprehensive evaluation of the proposed framework's performance.

Abstract

For decades, track association has been a challenging problem in marine surveillance, which involves the identification and association of vessel observations over time. However, the Automatic Identification System (AIS) has provided a new opportunity for researchers to tackle this problem by offering a large database of dynamic and geo-spatial information of marine vessels. With the availability of such large databases, researchers can now develop sophisticated models and algorithms that leverage the increased availability of data to address the track association challenge effectively. Furthermore, with the advent of deep learning, track association can now be approached as a data-intensive problem. In this study, we propose a Long Short-Term Memory (LSTM) based multi-model framework for track association. LSTM is a recurrent neural network architecture that is capable of processing multivariate temporal data collected over time in a sequential manner, enabling it to predict current vessel locations from historical observations. Based on these predictions, a geodesic distance based similarity metric is then utilized to associate the unclassified observations to their true tracks (vessels). We evaluate the performance of our approach using standard performance metrics, such as precision, recall, and F1 score, which provide a comprehensive summary of the accuracy of the proposed framework.