Doctoral Degrees (Logistics)

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Browsing Doctoral Degrees (Logistics) by Author "Du Toit, Jacques"

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    Decision support for threat detection in maritime surveillance
    (Stellenbosch : Stellenbosch University, 2014-12) Du Toit, Jacques; Van Vuuren, Jan Harm; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Logistics.
    ENGLISH ABSTRACT: The policing and monitoring of South Africa's coastline and economic exclusion zone is made di cult not only by the size of the area of interest, but also by the limited resources available for maritime detection and policing. As a consequence, illegal activities, such as smuggling, poaching and illegal border crossings, are often conducted with impunity. Conventional approaches to monitoring coastal areas, such as the use of patrol boats, port inspections and aircraft surveillance, may be augmented by advances in technology that are steadily contributing vast amounts of data related to maritime activity. For example, various South African agencies collect auto- matic identi cation system and vessel monitoring system transmissions, and gather additional kinematic data of maritime vessels through a number of strategically placed coastal radars. A command and control centre for actively monitoring these data (outside of the intelligence community) was established by the South African Navy in 2014. Such centres provide surveillance operators with a real-time picture of a maritime region of interest from which they can identify relevant facts of interest through a reliance on experience and domain knowledge. The e ectiveness of this process may, however, be undermined by the vast quantities of data typically under consideration, by the di culty of identifying long-term trends in vessel kinematic behaviour and by the possibility of operator fatigue brought on by the relatively low incidence levels of activities of interest. E ective decision support tools may play a valuable role in this context by the automatic processing of these vast collections of data, by the identi cation of concepts of interest and by the prediction of future occurrences of interest. It is, however, essential that such tools should be exible enough to adapt to changes in typical vessel behaviour over time and that they should be capable of integrating new trends and new types of behaviours. Various approaches to maritime surveillance are investigated in this dissertation from the perspectives of threat detection and anomaly identi cation, with particular emphasis on a systems approach to decision support. A decision support system framework that utilises rule-based and data-driven mechanisms is proposed as a means to separate the interesting from the uninteresting and to provide early warnings of potentially threatening maritime vessel behaviour to operators. This system framework is primarily concerned with kinematic data and is restricted to the identi cation of certain types of activities. Successful classi cation and, ultimately, timely prediction of potentially threatening behaviour would allow for e ective policing by providing early warning to relevant entities, thus potentially leading to more e ective use of available policing resources.