Clustering of Multiple Ships Underwater Radiated Noise

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The increase in shipborne underwater radiated noise (URN) is considered a source of pollution, and should be monitored regularly. We identify knowledge gap in the URN monitoring of vessels of opportunity, where the recording may include URN from multiple vessels simultaneously. To that end, we proposed a method to distinguish, by clustering, between narrow-band tonal lines originated from multiple vessels as received by a single omnidirectional hydrophone. Our clustering is based on feature extraction to classify the tonal lines into vessels by clustering the tonal lines to groups based on their likelihood to be originated from the same source. We present proof-of-concept based on data collected from the "ShipsEar"database and a test-case that shows more than 75% in the true negative and true positive for vessel association.

Original languageEnglish
Title of host publicationOCEANS 2023 - Limerick, OCEANS Limerick 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350332261
DOIs
StatePublished - 2023
Event2023 OCEANS Limerick, OCEANS Limerick 2023 - Limerick, Ireland
Duration: 5 Jun 20238 Jun 2023

Publication series

NameOCEANS 2023 - Limerick, OCEANS Limerick 2023

Conference

Conference2023 OCEANS Limerick, OCEANS Limerick 2023
Country/TerritoryIreland
CityLimerick
Period5/06/238/06/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Multi-view spectral clustering
  • Shipping noise
  • Underwater radiated noise

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Oceanography
  • Automotive Engineering
  • Management, Monitoring, Policy and Law
  • Acoustics and Ultrasonics

Fingerprint

Dive into the research topics of 'Clustering of Multiple Ships Underwater Radiated Noise'. Together they form a unique fingerprint.

Cite this