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Detecting anomalous deviations from standard maritime routes using the Ornstein-Uhlenbeck process
(CMRE, 2019/05)
A novel anomaly detection procedure based on the Ornstein-Uhlenbeck (OU) mean-reverting stochastic process is presented. The considered anomaly is a vessel that deviates from a planned route, changing its nominal velocity ...
Hybrid Bernoulli filtering for detection and tracking of anomalous path deviations
(CMRE, 2019/05)
This paper presents a solution to the problem of sequential joint anomaly detection and tracking of a target subject to switching unknown path deviations. Based on a dynamic model described by Ornstein- Uhlenbeck (OU) ...
Maritime situational awareness use cases enabled by space-borne sensors
(CMRE, 2019/06)
This paper discusses a technique for predicting a vessel?s position over a long time horizon with much lower uncertainty than current methods. Lowering the uncertainty of long-range prediction is a key challenge in maritime ...
Long-term vessel kinematics prediction exploiting mean-reverting processes
(CMRE, 2019/06)
Long-term target state estimation of non-manoeuvring targets, such as vessels under way in open sea, is crucial for maritime security. The dynamics of non-manoeuvring targets is traditionally modelled with a white noise ...
A document-based data model for large scale computational maritime situational awareness
(CMRE, 2019/06)
Computational Maritime Situational Awareness (MSA) supports the maritime industry, governments, and international organizations with machine learning and big data techniques for analyzing vessel traffic data available ...
A distributed approach to estimating sea port operational regions from lots of AIS data
(CMRE, 2019/06)
Seaports play a vital role in the global economy, as they operate as the connection corridors to all other modes of transport and as engines of growth for the wider region. But ports today are faced with numerous unique ...
Modeling vessel kinematics using a stochastic mean-reverting process for a long-term prediction
(CMRE, 2019/06)
We present a novel method for predicting long-term target states based on mean-reverting stochastic processes. We use the Ornstein-Uhlenbeck (OU) process, leading to a revised target state equation and to a time scaling ...
Automated port traffic statistics: from raw data to visualization
(CMRE, 2017/11)
We describe how we leveraged best practices in big data processing pipeline design and visual analytics to prototype the Maritime Patterns-of-Life Information Service (MPoLIS), an information product currently under ...
Scalable distributed change detection and its application to maritime traffic
(CMRE, 2019/06)
Building on a novel methodology based on the Ornstein-Uhlenbeck (OU) process to perform accurate long-term predictions of future positions of ships at sea, we present a statistical approach to the detection of abrupt changes ...
Data driven vessel trajectory forecasting using stochastic generative models
(2019/05)
In this work, we propose a data driven trajectory forecasting algorithm that utilizes both recorded historical and streaming trajectory observations. The algorithm performs Bayesian inference on a directed graph the walks ...