Decision support for threat detection in maritime surveillance

[摘要] ENGLISH ABSTRACT: The policing and monitoring of South Africa's coastline and economic exclusion zone is madedi cult not only by the size of the area of interest, but also by the limited resources available formaritime detection and policing. As a consequence, illegal activities, such as smuggling, poachingand illegal border crossings, are often conducted with impunity. Conventional approaches tomonitoring 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 amountsof data related to maritime activity. For example, various South African agencies collect auto-matic identi cation system and vessel monitoring system transmissions, and gather additionalkinematic 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 intelligencecommunity) was established by the South African Navy in 2014.Such centres provide surveillance operators with a real-time picture of a maritime region ofinterest from which they can identify relevant facts of interest through a reliance on experienceand domain knowledge. The e ectiveness of this process may, however, be undermined by thevast quantities of data typically under consideration, by the di culty of identifying long-termtrends in vessel kinematic behaviour and by the possibility of operator fatigue brought on bythe relatively low incidence levels of activities of interest.E ective decision support tools may play a valuable role in this context by the automatic processingof these vast collections of data, by the identi cation of concepts of interest and by theprediction of future occurrences of interest. It is, however, essential that such tools should beexible enough to adapt to changes in typical vessel behaviour over time and that they shouldbe capable of integrating new trends and new types of behaviours.Various approaches to maritime surveillance are investigated in this dissertation from the perspectivesof threat detection and anomaly identi cation, with particular emphasis on a systemsapproach to decision support. A decision support system framework that utilises rule-based anddata-driven mechanisms is proposed as a means to separate the interesting from the uninterestingand to provide early warnings of potentially threatening maritime vessel behaviour tooperators. This system framework is primarily concerned with kinematic data and is restrictedto the identi cation of certain types of activities. Successful classi cation and, ultimately, timelyprediction of potentially threatening behaviour would allow for e ective policing by providingearly warning to relevant entities, thus potentially leading to more e ective use of availablepolicing resources.

[发布机构] Stellenbosch University