A Factor-Graph Clustering Approach for Detection of Underwater Acoustic Signals

Dror Kipnis; Roee DIamant

doi:10.1109/LGRS.2018.2879889

A Factor-Graph Clustering Approach for Detection of Underwater Acoustic Signals

Dror Kipnis, Roee DIamant

Department of Marine Technologies

Research output: Contribution to journal › Article › peer-review

Abstract

We address the challenge of detecting an arbitrary-shaped underwater acoustic signal. Instead of setting a detection threshold, which due to noise transients may result in a high false alarm rate (FAR), our method classifies each measured sample as either 'noise' or 'signal.' Utilizing a priori knowledge of only the minimal duration of the signal, the decision is made using loopy belief propagation over a factor graph. Numerical simulations and sea experimental results show that our scheme achieves a favorable tradeoff between the Recall and FAR, and noise robustness, which far exceeds that of benchmark schemes.

Original language	English
Article number	8579234
Pages (from-to)	702-706
Number of pages	5
Journal	IEEE Geoscience and Remote Sensing Letters
Volume	16
Issue number	5
DOIs	https://doi.org/10.1109/LGRS.2018.2879889
State	Published - May 2019

Bibliographical note

Funding Information:
Manuscript received July 4, 2018; revised September 20, 2018; accepted October 23, 2018. Date of publication December 17, 2018; date of current version April 22, 2019. This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Program under Grant 773753 (Symbiosis) and in part by the NATO Science for Peace and Security Program under Grant G5293. (Corresponding author: Dror Kipnis.) The authors are with the Department of Marine Technology, University of Haifa, Haifa 3498838, Israel (e-mail: dkipnis@campus.haifa.ac.il; roeed@univ.haifa.ac.il).

Publisher Copyright:
© 2004-2012 IEEE.

Keywords

Clustering
factor graphs
loopy belief propagation (LBP)
sea experiment
signal detection
underwater acoustics

ASJC Scopus subject areas

Geotechnical Engineering and Engineering Geology
Electrical and Electronic Engineering

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10.1109/LGRS.2018.2879889

Cite this

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abstract = "We address the challenge of detecting an arbitrary-shaped underwater acoustic signal. Instead of setting a detection threshold, which due to noise transients may result in a high false alarm rate (FAR), our method classifies each measured sample as either 'noise' or 'signal.' Utilizing a priori knowledge of only the minimal duration of the signal, the decision is made using loopy belief propagation over a factor graph. Numerical simulations and sea experimental results show that our scheme achieves a favorable tradeoff between the Recall and FAR, and noise robustness, which far exceeds that of benchmark schemes.",

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A Factor-Graph Clustering Approach for Detection of Underwater Acoustic Signals

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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