— DHS Centers of Excellence Science and Technology Student Papers —

Decision Learning Algorithm for Acoustic Vessel Classification

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AUTHOR:
Talmor Meir

Talmor (Tal) Meir is a graduate student in ocean engineering at Stevens Institute of Technology. She earned her Bachelor of Science in geophysics from Tel-Aviv University of Israel. At Tel-Aviv University she worked as a research assistant for the Department of Remote Sensing. Her interests include forecast modeling of urban surroundings and the communication and visual interchange of imperative data. In the summer of 2010, Tal took part in the CSR Summer Institute Research (SRI) with a concentration in acoustics for the maritime security domain. Within this context she is currently working on ship classification methods in the metropolitan area of New York City conducted at the Maritime Security Laboratory at Stevens Institute of Technology. She may be contacted at tmeir@stevens.edu.

AUTHOR:
Mikhail Tsionskiy



AUTHOR:
Alexander Sutin



AUTHOR:
Hady Salloum



ABSTRACT:
Detection, tracking and classifying vessels of all sizes approaching ports and harbors is an imperative aspect to the security of complex maritime systems. This case study is an application of the passive acoustic method for vessel classification. The analysis of noise radiated by passing boats in Hudson River provides sound signatures and specific acoustic features of various boats. The features are then implemented into a decision-making algorithm used for final classification.

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SUGGESTED CITATION:
Meir, Talmor et al. “Decision Learning Algorithm for Acoustic Vessel Classification.” Homeland Security Affairs, DHS Centers of Excellence Science and Technology Student Papers (March 2012)
http://www.hsaj.org/?article=0.4.3
http://www.hsaj.org/