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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 ...
Multiple sensor Bayesian extended target tracking fusion approaches using random matrices
(CMRE, 2019/06)
The tracking of extended targets is attracting a growing literature thanks to the high resolution of several modern radar systems. A fully Bayesian solution has been proposed in the random matrix framework. In this paper, ...
A scalable algorithm for tracking an unknown number of targets using multiple sensors
(CMRE, 2019/06)
We propose an algorithm for tracking an unknown number of targets based on measurements provided by multiple sensors. Our algorithm achieves low computational complexity and excellent scalability by running belief propagation ...
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 ...
Multiple sensor measurement updates for the extended target tracking random matrix model
(CMRE, 2019/06)
In this paper, multiple sensor measurement update is studied for a random matrix model. Four different updates are presented and evaluated: three updates based on parametric approximations of the extended target state ...
Multiple Bayesian extended target tracking
(CMRE, 2019/06)
To track an extended target presents challenges because the hypothesis of "one target means one detection" is not valid. Several approaches to extended target tracking (ETT) have been found promising, and in particular ...
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 ...
Tracking an unknown number of targets using multiple sensors: a belief propagation method
(CMRE, 2019/06)
We propose a multisensor method for tracking an unknown number of targets. Low computational complexity and very good scalability in the number of targets, number of sensors, and number of measurements per sensor are ...
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 ...