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Now showing items 11-20 of 23
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 ...
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 ...
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 ...
Bayesian track-to-graph association for maritime traffic monitoring
(CMRE, 2019/05)
We present a hypothesis test to associate ship track measurements to an edge of a given graph that statistically models common traffic routes in a given area of interest. The association algorithm is based on the hypothesis ...
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 ...
Adaptive filtering of imprecisely time-stamped measurements with application to AIS networks
(CMRE, 2019/06)
Driven by real-world issues in maritime surveillance, we consider the problem of estimating the target state from a sequence of observations that can be imprecisely time-stamped. That is, the time between two consecutive ...
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 ...
Multiple Ornstein-Uhlenbeck processes for maritime traffic graph representation
(CMRE, 2019/05)
We propose an unsupervised procedure to automatically extract a graph-based model of commercial maritime traffic routes from historical Automatic Identification System (AIS) data. In the proposed representation, the main ...