Knowledge-based vessel position prediction using historical AIS data

Mazzarella, Fabio; Arguedas, Virginia Fernández; Vespe, Michele

doi:10.1109/sdf.2015.7347707

Cited by 90 publications

(65 citation statements)

References 11 publications

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“…Statistical methods have been applied to AIS data for maritime traffic probabilistic forecasting (Xiao et al, 2017), analysis of motion patterns (Ristic et al, 2008), and position prediction using historical AIS data (Mazzarella et al, 2015).…”

Section: Motion Predictionmentioning

confidence: 99%

eXogenous Kalman Filter (XKF) for Visualization and Motion Prediction of Ships using Live Automatic Identification System (AIS) Data

Fossen¹,

Fossen²

2018

MIC

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This paper addresses the problem of ship motion estimation using live data from Automatic Identification Systems (AIS). A globally exponentially stable observer for visualization and motion prediction of ships has been designed. Instead of using the extended Kalman filter (EKF) to deal with the kinematic nonlinearities the eXogenous Kalman Filter (XKF) is applied and by this global stability properties are proven.The proposed observer was validated using live AIS data from the Trondheim harbor in Norway and it was demonstrated that the observer tracks ships in real time. It was also demonstrated that the observer can predict the future motion of ships.The motion prediction capabilities are very useful for decision-support systems since this can be used to improve situational awareness e.g. for manned and unmanned autonomous ships that operate in common waters.

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Section: Motion Predictionmentioning

confidence: 99%

eXogenous Kalman Filter (XKF) for Visualization and Motion Prediction of Ships using Live Automatic Identification System (AIS) Data

Fossen¹,

Fossen²

2018

MIC

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“…• Current methods do not explicitly address the irregular time-sampling of AIS streams. Non-sequential methods [14] do not take it into account and sequential ones [7] assume they are provided with regularly-sampled streams, which is not true or may result in the creation of artificial, possibly erroneous AIS positions if interpolation techniques are used as a pre-processing step. As detailed hereafter, we develop a novel multi-task deep learning framework to address these issues and demonstrate its relevance from experiments on a real AIS dataset on a regional scale.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Task Deep Learning Architecture for Maritime Surveillance Using AIS Data Streams

Nguyen

Vadaine

Hajduch

et al. 2018

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)

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In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular timesampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification.

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“…It should be relatively simple, therefore, for a receiver to predict the senderʹs approximate location at the next transmission. Predictive AIS has been described by a number of sources as a way to use historic AIS information to predict the path of other vessels (Hexeberg, Flåten, Eriksen, & Brekke, 2017;Last, Bahlke, Hering-Bertram, & Linsen, 2014;Mazzarella, Arguedas, & Vespe, 2015), and these methods are already used for research and practice for additional collision avoidance techniques and better understanding of traffic patterns. But if a station stores the position at just the last transmission, it can predict a range where the sender should be at the next transmission.…”

Section: Future Researchmentioning

confidence: 99%

A Taxonomy Framework for Maritime Cybersecurity: A Demonstration Using the Automatic Identification System

Kessler¹,

Craiger²,

Haass³

2018

TransNav

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The maritime transportation system is increasingly a target of cyber attacks. This paper describes a taxonomy that supports the creation of adversarial cyber models, risk mitigation, and resiliency plans as applied to the maritime industry, using the Automatic Identification System as a specific illustration of the approach. This method has already been applied to the aviation sector; retooling it for a maritime example demonstrates its broad applicability to the transportation sector, in general. http://www.transnav.eu the International Journal on Marine Navigation and Safety of Sea Transportation Volume 12 Number 3

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Knowledge-based vessel position prediction using historical AIS data

Cited by 90 publications

References 11 publications

eXogenous Kalman Filter (XKF) for Visualization and Motion Prediction of Ships using Live Automatic Identification System (AIS) Data

eXogenous Kalman Filter (XKF) for Visualization and Motion Prediction of Ships using Live Automatic Identification System (AIS) Data

A Multi-Task Deep Learning Architecture for Maritime Surveillance Using AIS Data Streams

A Taxonomy Framework for Maritime Cybersecurity: A Demonstration Using the Automatic Identification System

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