Decision support for threat detection in maritime surveillance

Du Toit, Jacques (2014-12)

Thesis (PhD)--Stellenbosch University, 2014.

Thesis

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.

AFRIKAANSE OPSOMMING: Die patrollering en monitering van die Suid-Afrikaanse kusgebied en gepaardgaande ekonomiese eksklusiewe zone word bemoeilik deur die grootte van die tersprake area en die beperkte hulpbronne wat vir patrollie-doeleindes aangewend kan word. Gevolglik gaan onwettige aktiwiteite, soos smokkelary, stroping en onwettige immigrasie dikwels ongestraf. Konvensionele benaderings tot die monitering van kusgebiede, soos die aanwending van patrolliebote, die uitvoer van hawe-inspeksies en gere elde lugpatrollies, kan aangevul word deur tegnologiese vooruitgang wat voortdurend tot groot hoeveelhede data oor maritieme aktiwiteit bydra. Verskeie Suid- Afrikaanse agentskappe ontvang byvoorbeeld outomatiese identi kasiestelsel en vaartuigmoni- teringstelsel uitsendings, en samel ook addisionele kinematiese data oor maritieme vaartuie deur middel van strategies-geplaasde kusradars in. 'n Bevel-en-beheersentrum wat hierdie inligting (buite die intelligensiegemeenskap) aktief ontleed, is in 2014 deur die Suid-Afrikaanse Vloot tot stand gebring. Sulke sentra verskaf 'n intydse blik oor die maritieme gebied onder beskouing aan operateurs wat dan, gebaseer op hulle ervaring en omgewingskennis, relevante inligting oor vaartuie kan a ei. Die doeltre ende uitvoering van hierdie proses kan egter ondermyn word deur die tipiese groot hoeveelhede data, die moeilikheidsgraad van die identi kasie van langtermyn tendense in die kinematiese gedrag van vaartuie om die kus en die moontlikheid van operateur-uitputting as gevolg van lang periodes van relatiewe oninteressante vaartuiggedrag. Doeltre ende besluitsteunhulpmiddels kan 'n waardevolle bydrae in hierdie konteks maak deur die ge-outomatiseerde prosessering van hierdie groot hoeveelhede data, die identi kasie van interessante vaartuiggedrag en die voorspelling van toekomstige relevante insidente. Dit is egter noodsaaklik dat sulke hulpmiddels buigsaam genoeg moet wees om te kan aanpas by veranderings in tipiese maritieme aktiwiteit oor tyd en dat nuwe tendense en tipes aktiwiteite geakkommodeer kan word. Verskeie benaderings tot maritieme oorsig word in hierdie proefskrif vanuit die perspektiewe van die bespeuring van bedreigings en die opsporing van vreemde verskynsels ondersoek, met 'n spesi eke fokus op 'n stelselbenadering tot besluitsteun. 'n Besluitsteun stelselraamwerk wat berus op re el-gebaseerde en data-aangedrewe meganismes word as 'n hulpmiddel voorgestel waarmee interessante maritieme gedrag van oninteressante gedrag onderskei kan word om sodoende 'n vroe e waarskuwing aan operateurs met betrekking tot moontlike bedreigende maritieme aktiwiteite te kan rig. Die werking van hierdie stelselraamwerk berus hoofsaaklik op die gebruik van kinematiese vaartuigdata en is beperk tot die naspeuring van sekere soorte bedreigende gedrag. Die suksesvolle klassi kasie en tydige voorspelling van potensi ele bedreigende maritieme gedrag behoort doeltre ende kusmonitering en verbeterde aanwending van die beperkte, gepaardgaande hulpbronne deur relevante kusagentskappe moontlik te maak.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/96128
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