A novel approach to real-time range estimation of underwater acoustic sources using supervised machine learning

The proposed paper introduces a novel method for range estimation of acoustic sources, both cetaceans and industrial sources, in deep sea environments using supervised learning with neural networks in the contex of a single sensor, a compact array, or a small aperture towed array. The presented resu...

Full description

Saved in:
Bibliographic Details
Published inOCEANS 2017 - Aberdeen pp. 1 - 5
Main Authors Houegnigan, Ludwig, Safari, Pooyan, Nadeu, Climent, van der Schaar, Mike, Andre, Michel
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2017
Subjects
Absorption
Acoustics
array processing
density estimation
Estimation
neural networks
Noise measurement
range estimation
Real-time systems
Reflection
Sonar equipment
source localization
Online AccessGet full text

Cover

More Information
Summary:The proposed paper introduces a novel method for range estimation of acoustic sources, both cetaceans and industrial sources, in deep sea environments using supervised learning with neural networks in the contex of a single sensor, a compact array, or a small aperture towed array. The presented results have potential both for industrial impact and for the conservation and density estimation of cetaceans. With an average error of 4.3% for ranges up to 8 kilometers and typically below 300 meters, those results are challenging and to our knowledge they are unprecedented for an automated real-time solution.
DOI:10.1109/OCEANSE.2017.8084914