Data Fusion For Situation Monitoring Incident Detection Alert And Response Management
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Data Fusion for Situation Monitoring, Incident Detection, Alert and Response Management
Data Fusion is an interdisciplinary technology domain. This work focuses on the mature phase of data fusion, namely the detection and identification/classification of phenomena being observed and exploitation of the related methods for Security-Related Civil Science and Technology (SST) applications.
Data Fusion for Situation Monitoring, Incident Detection, Alert and Response Management
Data Fusion is a very broad interdisciplinary technology domain. It provides techniques and methods for; integrating information from multiple sources and using the complementarities of these detections to derive maximum information about the phenomenon being observed; analyzing and deriving the meaning of these observations and predicting possible consequences of the observed state of the environment; selecting the best course of action; and controlling the actions. Here, the focus is on the more mature phase of data fusion, namely the detection and identification / classification of phenomena being observed and exploitation of the related methods for Security-Related Civil Science and Technology (SST) applications. It is necessary to; expand on the data fusion methodology pertinent to Situation Monitoring, Incident Detection, Alert and Response Management; discuss some related Cognitive Engineering and visualization issues; provide an insight into the architectures and methodologies for building a data fusion system; discuss fusion approaches to image exploitation with emphasis on security applications; discuss novel distributed tracking approaches as a necessary step of situation monitoring and incident detection; and provide examples of real situations, in which data fusion can enhance incident detection, prevention and response capability. In order to give a logical presentation of the data fusion material, first the general concepts are highlighted (Fusion Methodology, Human Computer Interactions and Systems and Architectures), closing with several applications (Data Fusion for Imagery, Tracking and Sensor Fusion and Applications and Opportunities for Fusion).
Prediction and Recognition of Piracy Efforts Using Collaborative Human-Centric Information Systems
Maritime piracy is the cause of widespread international concern, and the number of pirate attacks has increased substantially in recent years. Many commercial vessels are inherently vulnerable to attack because of their size and relative slowness, and technological improvements have resulted in smaller crews on large vessels, whilst the absence of enforcement agencies in international waters has served only to make pirates more daring. Collaborative human-centric information support systems can significantly improve the ability of every nation to predict and prevent pirate attacks, or to recognize the nature and size of an attack rapidly when prevention fails, and improve the collective response to an emergency. This book presents the papers delivered at the NATO Advanced Study Institute (ASI) Prediction and Recognition of Piracy Efforts Using Collaborative Human-Centric Information Systems, held in Salamanca, Spain, in September 2011. A significant observation from previous NATO Advanced Study Institutes and Workshops was that domain experts responsible for maritime security were not fully aware of the wide variety of technological solutions available to enhance their support systems, and that although technology experts have a general understanding of the requirements in security systems, they often lacked knowledge concerning the operational constraints affecting those who implement security procedures. This ASI involved both technology and domain experts, as well as students from related fields of study. It offered an opportunity for them to discuss the issues surrounding the prediction, recognition and deterrence of maritime piracy, and will be of interest to all those whose work is related to this internationally important issue.