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Pollara, Alexander.

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Characterization of Small Vessels from Acoustical Signatures.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Characterization of Small Vessels from Acoustical Signatures./
Author:	Pollara, Alexander.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	249 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Contained By:	Dissertation Abstracts International79-08B(E).
Subject:	Acoustics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10624722
ISBN:	9780355743449

Characterization of Small Vessels from Acoustical Signatures.
Pollara, Alexander.

Characterization of Small Vessels from Acoustical Signatures. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 249 p.

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2017.

Small boats are a major concern in the maritime security field. They account for more than 90\% of global vessel traffic. Despite this, there is no practical system for monitoring these vessels. Passive acoustic methods can detect, and track small vessels. The underwater sound of boats can also contain information about the type of vessel. Military groups use passive acoustics for anti-submarine warfare. Such systems are also very attractive for port and border security. Maritime security groups need a system to determine the type of vessel detected to make full use of passive acoustic technology. This dissertation pertains to the characterization of small surface vessels from underwater sound.

ISBN: 9780355743449Subjects--Topical Terms:

879105
Acoustics.

Characterization of Small Vessels from Acoustical Signatures.
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100 1 $a Pollara, Alexander. $3 3427208
245 1 0 $a Characterization of Small Vessels from Acoustical Signatures.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 249 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
500 $a Adviser: Alexander Sutin.
502 $a Thesis (Ph.D.)--Stevens Institute of Technology, 2017.
520 $a Small boats are a major concern in the maritime security field. They account for more than 90\% of global vessel traffic. Despite this, there is no practical system for monitoring these vessels. Passive acoustic methods can detect, and track small vessels. The underwater sound of boats can also contain information about the type of vessel. Military groups use passive acoustics for anti-submarine warfare. Such systems are also very attractive for port and border security. Maritime security groups need a system to determine the type of vessel detected to make full use of passive acoustic technology. This dissertation pertains to the characterization of small surface vessels from underwater sound.
520 $a Large ships have various mechanical systems which create complex sets of tones. There is a sizable amount of concurring research on the sound produced by large ships. There is no similar consensus or body of work about the underwater sound made by small boats. Stevens has made acoustic recordings in the Port of Miami and Lake Hopatcong in New Jersey. This dissertation uses these recordings to analyze the underwater sound of small vessels. This analysis shows that it is possible to connect a boats mechanical systems with the sounds they make. We show that it is also possible to solve the inverse problem: determining the mechanical systems of a boat from detected sounds. This process is referred to as characterization.
520 $a The characterization process uses several algorithms. Some, like the signal spectra, Detection of Envelope Modulation on Noise (DEMON), and Cepstrum algorithm, are well known. Other algorithms are derived here for the first time. We present a new tone finding algorithm using gradient kernels in the spectrogram. This dissertation also introduces the "Phase DEMON" algorithm. Phase DEMON is a novel tool for finding the angle of arrival of individual tones in the DEMON spectrum. Using this algorithm we show a new method to determine when tones in a spectrogram come from the same boat. Isolating tones from an individual vessel is an important precondition for characterization.
520 $a The research also discovered several new phenomena in the acoustic signatures of small boats. High frequency noise modulated by engine tones was observed for the first time. This noise was analyzed with the Cyclic Modulations Spectrum, an algorithm which displays frequencies of modulation and their distribution across the high frequency spectrum of carrier noise.
520 $a The algorithms and findings in this dissertation represent significant progress towards an automatic passive acoustic solution for extracting identifying information about small boats.
590 $a School code: 0733.
650 4 $a Acoustics. $3 879105
650 4 $a Ocean engineering. $3 660731
650 4 $a Statistics. $3 517247
690 $a 0986
690 $a 0547
690 $a 0463
710 2 $a Stevens Institute of Technology. $b Maritime Systems. $3 3427209
773 0 $t Dissertation Abstracts International $g 79-08B(E).
790 $a 0733
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10624722