Dissertations / Theses on the topic 'Maritime situation awareness'

In this study, a method is developed for modelling and classifying behaviour of maritime vessels in a piracy situation. Prior knowledge is used to construct a probabilistic graphical model of maritime vessel behaviour. This model is a novel variant of a dynamic Bayesian network (DBN), that extends the switching linear dynamic system (SLDS) to accommodate contextual information. A generative model and a classifier model are developed. The purpose of the generative model is to generate simulated data by modelling the behaviour of fishing vessels, transport vessels and pirate vessels in a maritime piracy situation. The vessels move, interact and perform various activities on a predefined map. A novel methodology for evaluating and optimising the generative model is proposed. This methodology can easily be adapted to other applications. The model is evaluated by comparing simulation results with 2011 pirate attack reports. The classifier model classifies maritime vessels into predefined categories according to their behaviour. The classification is performed by inferring the class of a vessel as a fishing, transport or pirate vessel class. The classification method is evaluated by classifying the data generated by the generative model and comparing it to the true classes of the simulated vessels.
Thesis (PhD)--University of Pretoria, 2014.
tm2015
Electrical, Electronic and Computer Engineering
PhD
Unrestricted

The maritime industry is experiencing one of its longest and fastest periods of growth. Hence, the global maritime surveillance capacity is in a great need of growth as well. The detection of vessel activity is an important objective of the civil security domain. Detecting vessel activity may become problematic if audit data is uncertain. This thesis aims to investigate if Bayesian networks acquired from expert knowledge can detect activities with a signature-based detection approach. For this, a maritime pilot-boat scenario has been identified with a domain expert. Each of the scenario’s activities has been divided up into signatures where each signature relates to a specific Bayesian network information node. The signatures were implemented to find evidences for the Bayesian network information nodes. AIS-data with real world observations have been used for testing, which have shown that it is possible to detect the maritime pilot-boat scenario based on the taken approach.

 

The international maritime industry is growing fast due to an increasing number of transportations over sea. In pace with this development, the maritime surveillance capacity must be expanded as well, in order to be able to handle the increasing numbers of hazardous cargo transports, attacks, piracy etc. In order to detect such events, anomaly detection methods and techniques can be used. Moreover, since surveillance systems process huge amounts of sensor data, anomaly detection techniques can be used to filter out or highlight interesting objects or situations to an operator. Making decisions upon large amounts of sensor data can be a challenging and demanding activity for the operator, not only due to the quantity of the data, but factors such as time pressure, high stress and uncertain information further aggravate the task. Bayesian networks can be used in order to detect anomalies in data and have, in contrast to many other opaque machine learning techniques, some important advantages. One of these advantages is the fact that it is possible for a user to understand and interpret the model, due to its graphical nature.

This thesis aims to investigate how the output from a Bayesian network can be explained to a user by first reviewing and presenting which methods exist and second, by making experiments. The experiments aim to investigate if two explanation methods can be used in order to give an explanation to the inferences made by a Bayesian network in order to support the operator’s situation awareness and decision making process when deployed in an anomaly detection problem in the maritime domain.

 

Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, March 2009.
Thesis Advisor(s): Bordetsky, Alex ; Bourakov, Eugene. "March 2009." Description based on title screen as viewed on April 23, 2009. Author(s) subject terms: Global Positioning System, GPS, Situational Awareness, Maritime Domain Awareness. Includes bibliographical references (p. 55-57). Also available in print.

Thesis (M.S. in Systems Technology (Command, Control, and Communications))--Naval Postgraduate School, June 2010.
Thesis Advisor(s): Gallup, Shelley P.; Second Reader: MacKinnon, Douglas J. "June 2010." Description based on title screen as viewed on July 14, 2010. Author(s) subject terms: Machine Autonomy, AVS, USV, UUV, Navy Tactical Task List, Situational Awareness, Congruence Model, Decision Superiority, Maritime ISR. Includes bibliographical references (p. 49-51). Also available in print.

Dans une économie globalisée, le secteur maritime est essentiel au bon fonctionnement des économies et permet d’acheminer 90% des marchandises. Dans un contexte de forte numérisation, le niveau de cybersécurité du secteur maritime reste en retrait par rapport aux autres secteurs d'activité d'importance vitale. Au travers d’une analyse de bout en bout, cette thèse décrit les particularités des systèmes d’information maritimes et modélise le concept de Maritime Cyber Situational Awareness. Ensuite, une proposition d’architecture est décrite pour permettre l’acquisition de cet état de connaissance. Cette solution, éprouvée sur plate-forme, a permis de répondre à l’ensemble des critères d’élaboration. Enfin, les travaux soulignent et détaillent les spécificités du monde maritime pour tirer un profit maximal des données issues de la cybersurveillance. Les analyses et architectures de cette étude dans un contexte contraint pourront probablement être élargies à d’autres secteurs, comme par exemple les véhicules autonomes ou encore l’Internet des objets
In a globalized economy, the maritime sector plays an essential role for the countries’ economies, drawing 90% of the global world trade. In a highly digitalized transformation context, the cybersecurity level of the maritime sector remains low compared to other essential sectors. Through an end-to-end analysis, this thesis aims at describing the unique combined characteristics of maritime information systems. Then, we apply situational awareness definition to maritime cybersecurity and model the concept of Maritime Cyber Situational Awareness. Then we describe the proposal of an architecture to achieve MCSA elaboration, which has been tested and proven on our experimental platform, taking into account the full requirements. Our work then analyses the particularities of the maritime world to streamline the collected data. The analysis and architectures of this study could also be opened and applied to other sectors, such as autonomous vehiclesand the Internet of Things (IoT)

This thesis presents a framework capable of integrating hard (physics-based) and soft (people-generated) data for the purpose of achieving increased situational assessment (SA) and effective course of action (CoA) generation upon risk identification. The proposed methodology is realized through the extension of an existing Risk Management Framework (RMF). In this work, the RMF’s SA capabilities are augmented via the injection of soft data features into its risk modeling; the performance of these capabilities is evaluated via a newly-proposed risk-centric information fusion effectiveness metric. The framework’s CoA generation capabilities are also extended through the inclusion of people-generated data, capturing important subject matter expertise and providing mission-specific requirements. Furthermore, this work introduces a variety of CoA-related performance measures, used to assess the fitness of each individual potential CoA, as well as to quantify the overall chance of mission success improvement brought about by the inclusion of soft data. This conceptualization is validated via experimental analysis performed on a combination of real- world and synthetically-generated maritime scenarios. It is envisioned that the capabilities put forth herein will take part in a greater system, capable of ingesting and seamlessly integrating vast amounts of heterogeneous data, with the intent of providing accurate and timely situational updates, as well as assisting in operational decision making.

Sjöräddning i Sverige sker genom samverkan mellan statliga verksamheter, kommuner och frivilligorganisationer för ett gemensamt mål att rädda personer som råkat i sjönöd. Tid är ofta en kritisk faktor i räddningsuppdragen men ett snabbt och oplanerat utryck riskerar samtidigt att sätta räddningsaktörerna själva i farozonen. Utvecklingen av obemannade autonoma farkoster för SAR (eng. Search And Rescue) ses som en lösning på behovet att kunna snabbt skicka hjälp till samt få ögon på incidentplatsen utan att försätta räddningsaktörerna för onödig risk. Nuvarande kommunikationssystem inom svensk sjöräddning kan dock inte hantera annan typ av information än muntlig varav räddningsaktörer endast känner till riktlinjer för hantering av muntlig information. Med ett framtida införande av autonoma farkoster kommer dock fler informationstyper att behöva hanteras i sjöräddningar varav oklarheten om vilka informationstyper autonoma farkoster samlar in och vilka datahanteringskrav som finns är problematiskt. Oklarhet om informationstyperna och deras datahanteringskrav är vidare problematiskt för utvecklingen och implementeringen av autonoma farkoster då risken finns att farkoster och tekniker utvecklas men inte får användas för att de inte är anpassade efter lagkraven på hantering av olika datatyper. I denna studie undersöks därför vilka informationstyper som autonoma farkoster kan samla in vid sjöräddning. Detta för att komma fram till vilka riktlinjer för datahantering som gäller vid sjöräddning med autonoma farkoster. Studien undersöker också vilka informationstyper som är kritiska för en SAR-sjöräddningssamverkan samt vilka informationsdelningsutmaningar som finns i dagens sjöräddning. Studien genomfördes i form av en kvalitativ fallstudie och har tillämpat ett socio-tekniskt systemperspektiv för att bättre se till helheten och besvara frågeställningarna. Resultatet av denna studie visar att autonoma farkoster kan samla in information om sin omgivning, vilket utgör grunden för att skapa en medvetenhet om situationen som är kritiskt för SAR-operationer, och kan även samla in information om sitt eget tillstånd. De lagverk som identifierats utgöra de huvudsakliga restriktionerna är kamerabevakningslagen, lagen för skydd av geografisk information, offentlighets- och sekretesslagen, GDPR och dataskyddslagen. Dessa lagverk innehåller riktlinjer för delning av information och personuppgiftsbehandling i SAR-sjöräddning. Kunskapsbidrag studien har genererat inkluderar bland annat identifiering av datatyper som kan samlas in av autonoma farkoster i en SAR-sjöräddning, och sannolikt andra typer av räddningsinsatser, och delning och hanteringskraven på de datatyperna i räddningsinsatser och därmed kunskap om vilka datatyper som är mest reglerade. Vidare kunskapsbidrag är kunskap om vilka informationstyper som är mest kritiska för SAR-sjöräddningar, och därför bör prioriteras att samlas in och delas, och identifieringen av utmaningar för informationsdelning mellan statliga verksamheter och frivilligorganisationer.
Maritime rescue in Sweden is performed through a cooperation between government agencies, municipalities and non-governmental organisations (NGOs) with the common goal of saving people in distress. Time is often a critical factor in the rescue missions but a fast and unplanned response may at the same time put the rescue workers in danger. The development of unmanned autonomous vehicles for SAR is seen as a solution to the need of being able to quickly sendhelp as well as get eyes on the scene of the incident without exposing the rescue workers for unnecessary risks. However, the current communications systems in Swedish maritime rescue are unable to handle any other type of information except verbal, meaning that rescue workers only know guidelines for handling verbal information. However, with a future implementation of autonomous vehicles, there will be a need to handle more information types in maritime rescue whereas the uncertainty regarding what kind of information autonomous vehicles collect and which data management requirements exist is problematic. The uncertainty about the information types and their data management requirements is also problematic for the development and implementation of autonomous vehicles as there is a risk that vehicles and technologies are developed but not allowed to be used because they are not adapted to the legal requirements on management of the different types of data. Therefore, in this study I examine what information types that autonomous vehicles can collect in a maritime rescue to find out what guidelines for data management that applies during a maritime rescue with autonomous vehicles. The study also examines what kind of information’s are critical for a SAR maritime rescue cooperation as well as what information sharing challenges exist in current maritime rescue. The study was performed as a qualitative case study and has used a socio-technical systems perspective so as to better see the overall picture and answer the research questions. The result shows that autonomous vehicles can collect information about their surroundings, which is the foundation for establishing situation awareness that is critical for SAR-operations, and that they can collect information about their own status. The main laws and regulations that have been identified as constituting the main restrictions are (translated from Swedish) the law of camera surveillance, the law for protection of geographical information, the public access to information and secrecy act, the GDPR and the data protection act. These contains guidelines for sharing information and the processing of personal data in SAR maritime rescue. The knowledge contributions of this study includes among others the identification of datatypes that can be collected by autonomous vehicles in SAR maritime rescue, and probably other types of rescue operations, and the sharing and management requirements on those datatypes in rescue operations and thus knowledge of what datatypes that are the most restricted. Further knowledge contributions is knowledge about which information types that are the most critical for SAR maritime rescue and thus should be prioritised for collection and sharing as well as the identification of challenges for information sharing between government agencies and NGOs.

Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, September 2005.
Thesis Advisor(s): Alex Bordetsky. Includes bibliographical references (p.65-66). Also available online.

Syftet med detta arbete är att undersöka omgivningsmedvetenhet hos besättningsmedlemmar som arbetar vid förtöjningsplatser. Olyckor vid förtöjning sker varje år. De som arbetar på förtöjningsplaten arbetar med stora krafter under en tidspress, därför är det intressant att undersöka en besättning som avgår och lägger till kaj mer än två gånger på ett dygn. Verktyget Omgivningsmedvetenhet (engelska: situational awareness) kommer ifrån sjöfartens bryggtjänstgörning (engelska: Bridge resource Management) och är ett verktyg för att förbättra säkerheten. Undersökningen gjordes på ett kvalitativt sätt med semistrukturerade intervjuer för att lättare kunna fånga upp respondenternas egna synpunkter och erfarenheter. Resultatet visar att omgivningsmedvetenheten manifesteras genom att besättningsmedlemmarna är uppmärksamma och vaksamma på till exempel om någon kollega står i en riskzon eller om en tross är för sliten. Riskområden ansågs vara där det är störst risk för snapback. Till exempel vid klys, pollare och bakom trumman vid förtöjningsspelet.
The purpose of this paper is to investigate situational awareness within the crew, working on the mooring station. Accidents at the mooring station occur regularly. Those who are working at the mooring station handle large forces under a time pressure, therefore it is of interest to investigate the crew onboard vessels that moor and unmoor more than four times a day. The tool situational awareness comes from the maritime Bridge Resource Management, it is used to improve safety. This was a qualitative study and semi-structured interviews were carried out to make it easier to capture the respondents’ thoughts and experiences. The results showed that situational awareness is manifested through the crew in the form of attention and vigilance. High areas were regarded as as those with a high potential for snapback. These areas were highlighted as those in the immediate proximity of fairleads fairleads, bollards and behind the drum where the mooring line is stored.

For the past few years, oceans have become once again, an important means of communication and transport. In fact, traffic density throughout the globe has suffered a substantial growth, which has risen some concerns. With this expansion, the need to achieve a high Maritime Situational Awareness (MSA) is imperative. At the present time, this need may be more easily fulfilled thanks to the vast amount of data available regarding maritime traffic. However, this brings in another issue: data overload. Currently, there are so many data sources, so many data to obtain information from, that the operators cannot handle it. There is a pressing need for systems that help to sift through all the data, analysing and correlating, helping in this way the decision making process. In this dissertation, the main goal is to use different sources of data in order to detect anomalies and contribute to a clear Recognised Maritime Picture (RMP). In order to do so, it is necessary to know what types of data exist and which ones are available for further analysis. The data chosen for this dissertation was Automatic Identification System (AIS) and Monitorização Contínua das Atividades da Pesca (MONICAP) data, also known as Vessel Monitoring System (VMS) data. In order to store 1 year worth of AIS and MONICAP data, a PostgreSQL database was created. To analyse and draw conclusions from the data, a data mining tool was used, namely, Orange. Tests were conducted in order to assess the correlation between data sources and find anomalies. The importance of data correlation has never been so important and with this dissertation the aim is to show that there is a simple and effective way to get answers from great amounts of data.
Nos últimos anos, os oceanos tornaram-se, mais uma vez, um importante meio de comunicação e transporte. De facto, a densidade de tráfego global sofreu um crescimento substancial, o que levantou algumas preocupações. Com esta expansão, a necessidade de atingir um elevado Conhecimento Situacional Marítimo (CSM) é imperativa. Hoje em dia, esta necessidade pode ser satisfeita mais facilmente graças à vasta quantidade de dados disponíveis de tráfego marítimo. No entanto, isso leva a outra questão: sobrecarga de dados. Atualmente existem tantas fontes de dados, tantos dados dos quais extrair informação, que os operadores não conseguem acompanhar. Existe uma necessidade premente para sistemas que ajudem a escrutinar todos os dados, analisando e correlacionando, contribuindo desta maneira ao processo de tomada de decisão. Nesta dissertação, o principal objetivo é usar diferentes fontes de dados para detetar anomalias e contribuir para uma clara Recognised Maritime Picture (RMP). Para tal, é necessário saber que tipos de dados existem e quais é que se encontram disponíveis para análise posterior. Os dados escolhidos para esta dissertação foram dados Automatic Identification System (AIS) e dados de Monitorização Contínua das Atividades da Pesca (MONICAP), também conhecidos como dados de Vessel Monitoring System (VMS). De forma a armazenar dados correspondentes a um ano de AIS e MONICAP, foi criada uma base de dados em PostgreSQL. Para analisar e retirar conclusões, foi utilizada uma ferramenta de data mining, nomeadamente, o Orange. De modo a que pudesse ser avaliada a correlação entre fontes de dados e serem detetadas anomalias foram realizados vários testes. A correlação de dados nunca foi tão importante e pretende-se com esta dissertação mostrar que existe uma forma simples e eficaz de obter respostas de grandes quantidades de dados

Portugal tem uma das maiores Zonas Económicas Exclusivas a nível mundial, encerrando este espaço marítimo uma riqueza que ainda não se encontra devidamente aferida, mas que se julga ser enorme. Por ela passam anualmente milhares de navios, com os mais diversos destinos e transportando as mais variadas cargas. A posição geostratégica do país coloca-o no centro de algumas das mais movimentadas rotas marítimas, sendo por isso de extrema importância vigiar e monitorizar as águas portuguesas, por forma a garantir que as leis e regulamentos de direito internacional marítimo são cumpridos e que o interesse nacional é devidamente salvaguardado. Deste modo, a presente dissertação tem como objeto de estudo os sistemas de vigilância e monitorização marítimos, pretendendo constituir-se como um contributo para a melhoria do atual sistema de vigilância e monitorização dos espaços marítimos sob soberania ou jurisdição portuguesa, focando-se, para tal, nos sistemas aéreos e espaciais para a deteção de meios de superfície. Para tal, numa primeira parte considera-se estudar o ambiente marítimo e as ameaças que o afetam. Na segunda parte estudam-se os atuais sistemas que contribuem para o conhecimento situacional marítimo em Portugal culminando na terceira parte com o estudo dos meios e sensores que permitem melhorar a cobertura do espaço marítimo, com o objetivo final de garantir a segurança no mar. Através do estudo realizado foi possível concluir-se que as aeronaves não tripuladas afiguram-se como o futuro mais imediato para o esclarecimento do panorama marítimo, sendo que os satélites surgem numa segunda linha, pois apesar dos seus custos mais elevados, poderão também dar um enorme contributo para o conhecimento situacional marítimo ao serem capazes de cobrir maiores áreas e mais rapidamente.
Portugal has one of the largest Economic Exclusion Zones worldwide, enclosing a maritime space full of unknown and uncountable riches. Due to its geostrategic position, Portugal is the center of some of the mos busiest seaways where thousands of ships sail to and from different ports, carrying all types of cargo. Because of that, it is of utmost importance to focus on surveillance and monitoring of its maritime space, in order to make sure that laws and regulations of the International Maritime Law are being met. The purpose of this thesis is to study maritime surveillance and monitoring systems that can contribute to improve the current surveillance and monitoring system of the water spaces under Portuguese sovereignty or jurisdiction, focusing on aerial and spatial systems for the detection of surface vehicles. In order to achieve the proposed objectives, this thesis is divided into three chapters. The maritime environment and its threats are studied in the first chapter. In the second chapter, the focus is on the surveillance systems that contribute to the maritime situational awareness in Portugal. Finally, alternatives to the current surveillance system for maritime coverage improvement are presente, with the final purpose of ensuring safety and security at sea. The main conclusion of this study is that, in a near future, unmanned aerial vehicles are the best solution to improve on situational awareness, wherein satellites appear second. Despite their higher costs, the ability to cover larger areas, faster, make the satellites a major contributor to maritime situational awareness.

O aumento de complexidade das atividades desenvolvidas no domínio marítimo vem trazer novos desafios aos estados, principalmente aos costeiros. Portugal não é exceção e vê-se obrigado a reagir às suas responsabilidades como estado de bandeira de um crescente número de navios mercantes no mundo. Assim, em Novembro de 2017, foi criado o Portuguese Navy Shipping Centre (PNSC) ou em português Célula de Aconselhamento, Acompanhamento e Defesa (AAD). Neste seguimento e alinhado com o projeto MARISA, a Marinha Portuguesa promoveu o desenvolvimento de um estudo para a definição de requisitos da Célula AAD, visando melhorar o Conhecimento Situacional Marítimo (CSM) a médio/curto prazo. Este estudo começa com uma revisão dos conceitos de Conhecimento Situacional (CS), para melhor perceber e analisar o conceito de CSM, adotado na Marinha Portuguesa. CSM é um conceito amplamente debatido no campo da vigilância marítima, contudo existem alguns autores com definições consolidadas de CS que não têm sido referenciadas. Neste trabalho serão tidos em conta 3 conceitos de CS e os modelos associados, relacionando-os com o modelo de CSM adotado pela Marinha Portuguesa, considerando as virtudes e defeitos de cada modelo. Depois desta análise comparativa, é proposta a metodologia de Endsley, Goal-Direted Task Analsys (GDTA) para a definição de requisitos da Célula AAD. Aplicando a metodologia referida, caracterizou-se a célula, efetuando-se cerca de 6 horas de observação e 11 entrevistas a operadores, decisores e providers, de forma a definir um conjunto de requisitos para a Célula. Os resultados são expostos e catalogados segundo o modelo de Endsley e o modelo de construção de CSM da Marinha Portuguesa. Uma análise dos resultados é então elaborada, de onde resultam um conjunto de conclusões, expostas no fim do trabalho assim como algumas sugestões importantes para trabalhos futuros.
The increasing complexity of activities in the maritime domain raised new challenges to all coastal states. In the case of Portugal, it is also required to promptly react to the needs of Portuguese flag vessels around the world. Therefore in November of 2017 was created the Portuguese Navy Shipping Centre (PNSC). In line with this, and with the MARISA Project, the Portuguese Navy developed a study, focused on the requirements assessment do PNSC, in order to improve Maritime Situational Awareness (MSA) in a medium/short term. The study started with a review of SA concept models, to better understand and analyse the MSA concept used by Portuguese Navy. Maritime Situational Awareness is a concept that has been widely discussed in the maritime surveillance field; on the other hand there are some authors with well-established definitions of Situational Awareness (SA) not often referenced. In this thesis, after address three SA concepts and associated models, they are later related with the current MSA model used by the Portuguese Navy, considering the strengths and weaknesses of each model. From this comparative analysis, the Goal directed Task Analysis (GDTA) methodology is proposed to determine requirements for the PNSC. After characterize the centre, de results are exposed, cataloguing the requirements according with the 3 level models of Endsley and the 3 fases model of MSA construction of the Portuguese Navy. An Analysis of the results is then made, taking some conclusions and living a path for future work

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.