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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 ...
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 ...
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 ...
Validation of the Ornstein-Uhlenbeck route propagation model in the Mediterranean Sea
(CMRE, 2019/06)
Traffic route analysis and prediction are both crucial to maritime security. The ability to predict a vessel position in the future is essential to provide information on upcoming events. However, accurate prediction along ...
Scalable and distributed sea port operational areas estimation from AIS data
(CMRE, 2019/06)
Seaports are spatial units that do not remain static over time. They are constantly in flux, evolving according to environmental and connectivity patterns both in size and operational capacity. As such any valid decision ...