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dc.contributor.advisorScrofani, James
dc.contributor.advisorTummala, Murali
dc.contributor.authorMcAbee, Ashley S. M.
dc.dateDec-13
dc.date.accessioned2014-02-18T23:39:07Z
dc.date.available2014-02-18T23:39:07Z
dc.date.issued2013-12
dc.identifier.urihttp://hdl.handle.net/10945/38977
dc.description.abstractTechniques for anomaly detection in the maritime domain by extracting traffic patterns from ship position data to generate atlases of expected ocean travel are developed in this thesis. An archive of historical data is used to develop a traffic density grid. The Hough transformation is used to extract linear patterns of elevated density from the traffic density grid, which can be considered the highways of the oceans. These highways collectively create an atlas that is used to define geographical regions of expected ship locations. Ship position reports are compared to the atlas of highways to flag as anomalous any ship that is not operating on an expected highway. The atlas generation techniques are demonstrated using automated information system (AIS) ship position data to detect highways in both open-ocean and coastal areas. Additionally, the atlas generation techniques are used to explore variability in ship traffic as a result of extreme weather and seasonal variation. Finally, anomaly detection is demonstrated by comparing AIS data from 2013 to the highways detected in the archive of data from 2012. The development of an automatic atlas generation technique that can be used to develop a definition of normal maritime behavior is the significant result of this thesis.en_US
dc.description.urihttp://archive.org/details/trafficpatternde1094538977
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleTraffic pattern detection using the Hough transformation for anomaly detection to improve maritime domain awarenessen_US
dc.typeThesisen_US
dc.contributor.secondreaderGarren, David
dc.contributor.departmentElectrical And Computer Engineering
dc.subject.authorMaritime Domain Awarenessen_US
dc.subject.authorHough Transformationen_US
dc.subject.authorAnomaly Detectionen_US
dc.subject.authorAutomated Information Systemen_US
dc.subject.authorPattern Extractionen_US
dc.description.recognitionOutstanding Thesisen_US
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameMaster Of Science In Electrical Engineeringen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineElectrical Engineeringen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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