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Now showing items 11-20 of 35
Knowledge-based ship tracking applied to HF surface wave radar data
(CMRE, 2019/06)
In recent years, low-power high-frequency surface-wave radars have received significant attention thanks to their over-the-horizon coverage capability and the continuous-time operation mode. These radars have become effective ...
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 ...
The impact of sea state on HF surface-wave radar ship detection and tracking performances
(CMRE, 2019/06)
Nowadays, we face an ever increasing interest in new technologies and solutions for the maritime surveillance (MS) domain. In such a context, oceanographic high-frequency surface-wave (HFSW) radars have started to get ...
Realistic extended target model for track before detect in maritime surveillance
(CMRE, 2019/06)
Traditional target tracking algorithms are generally fed a set of thresholded detections under the hypothesis that no more than one detection is generated by each single target. Improvements in modern radar systems have ...
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 ...
Localization of small surface vessels through acoustic data fusion of two tetrahedral arrays of hydrophones
(CMRE, 2014/01)
Detection and tracking of vessels is important in confined areas such as marine parks or harbors. Nowadays, the presence of ships can be accurately monitored either by radar or via AIS system, while small vessels, which ...
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 ...