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Performance assessment of vessel dynamic models for long-term prediction using heterogeneous data
(CMRE, 2019/06)
Ship traffic monitoring is a foundation for many maritime security domains, and monitoring system specifications underscore the necessity to track vessels beyond territorial waters. However, vessels in open seas are seldom ...
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 ...
Anomaly detection and tracking based on mean-reverting processes with unknown parameters
(CMRE, 2019/05)
Piecewise mean-reverting stochastic processes have been recently proposed and validated as an effective model for long-term object prediction. In this paper, we exploit the Ornstein-Uhlenbeck (OU) dynamic model to represent ...
Consistent estimation of randomly sampled Ornstein-Uhlenbeck process long-run mean for long-term target state prediction
(CMRE, 2019/06)
In this letter, we study the problem of estimating the long-run mean of the Ornstein-Uhlenbeck (OU) stochastic process and its effect on the long-term prediction of future vessel states, which is a crucial problem for ...
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 ...
Model performance assessment for long-term vessel prediction using HFSW radar data
(CMRE, 2019/06)
Vessels in open seas are seldom continuously observed. Thus, the problem of long-term vessel prediction becomes crucial. This paper focuses its attention on the performance assessment of the Ornstein- Uhlenbeck target ...