A spatiotemporal clustering approach to maritime domain awareness
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Authors
Tester, Kristofer A.
Subjects
Spatiotemporal Clustering
Maritime Domain Awareness
Maritime Domain Awareness
Advisors
Scrofani, Jim
Tummala, Murali
Date of Issue
2013-09
Date
Sep-13
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Spatiotemporal clustering is the process of grouping objects based on both their spatial and temporal similarity. This approach is useful when considering the distance between objects and how that distance changes through time. Spatiotemporal clustering analysis is applied to the maritime domain in this thesis, specifically to a defined area of water, during a period of time, in order to gain behavioral knowledge of vessel interactions and provide the opportunity to screen such interactions for further investigation. The proposed spatiotemporal clustering algorithm spatially clusters vessels in the water space using k-means clustering analysis, kinematically refines the clusters based on the similarity of vessel headings, speeds and the distance between them, and temporally analyzes the continuity of membership of the kinematic clusters through time to determine which clusters are moving. The algorithm is implemented in the MATLAB programming environment, verified with a synthetic data scenario, and validated with two real-world datasets.
Type
Thesis
Description
Series/Report No
Department
Electrical and Computer Engineering (ECE)
Organization
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NPS Report Number
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Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This 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.