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Numerous national and multinational initiatives in maritime surveillance have been initiated, with the goal of having knowledge of all coastal and open-seas activities relevant to national security. As part of this effort, NATO is pursuing research activities to exploit existing multi-sensor systems in support of maritime surveillance. Multi-sensor fusion of data from maritime surveillance assets...
The paper is devoted to statistical analysis of vessel motion patterns in the ports and waterways using AIS ship self-reporting data. From the real historic AIS data we extract motion patterns which are then used to construct the corresponding motion anomaly detectors. This is carried out in the framework of adaptive kernel density estimation. The anomaly detector is then sequentially applied to the...
Change detection is an important task for remote monitoring, fault diagnostics and system prognostics. When a fault occurs, it will often times cause changes in measurable quantities of the system. Early detection of changes in system measurements that indicate abnormal conditions helps the diagnostics of the fault so that appropriate maintenance action can be taken before the fault progresses, causing...
Surveillance of large land, air or sea areas with a multitude of sensor and sensor types typically generates huge amounts of data. Human operators trying to establish individual or collective maritime situation awareness are often overloaded by this information. In order to help them cope with this information overload, we have developed a combined methodology of data visualization, interaction and...
In this paper we propose an approach for detecting anomalies in data from visual surveillance sensors. The approach includes creating a structure for representing data, building ldquonormal modelsrdquo by filling the structure with data for the situation at hand, and finally detecting deviations in the data. The approach allows detections based on the incorporation of a priori knowledge about the...
In this paper, unsupervised clustering of normal vessel traffic patterns is proposed and implemented, where patterns are represented as the momentary location, speed and course of tracked vessels. The learnt cluster models are used for anomaly detection in sea traffic. The Gaussian Mixture Model is used as cluster model and a greedy version of the Expectation-Maximization algorithm is used as clustering...
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