Direct Localization in Underwater Acoustics Via Convolutional Neural Networks: A Data-Driven Approach

Amir Weiss, Toros Arikan, Gregory W. Wornell

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

3 Scopus citations

Abstract

Direct localization (DLOC) methods, which use the observed data to localize a source at an unknown position in a one-step procedure, generally outperform their indirect two-step counterparts (e.g., using time-difference of arrivals). However, underwater acoustic DLOC methods require prior knowledge of the environment, and are computationally costly, hence slow. We propose, what is to the best of our knowledge, the first data-driven DLOC method. Inspired by classical and contemporary optimal model-based DLOC solutions, and leveraging the capabilities of convolutional neural networks (CNNs), we devise a holistic CNN-based solution. Our method includes a specifically-tailored input structure, architecture, loss function, and a progressive training procedure, which are of independent interest in the broader context of machine learning. We demonstrate that our method outperforms attractive alternatives, and asymptotically matches the performance of an oracle optimal model-based solution.

Original languageEnglish
Title of host publication2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing, MLSP 2022
PublisherIEEE Computer Society
ISBN (Electronic)9781665485470
DOIs
StatePublished - 2022
Externally publishedYes
Event32nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2022 - Xi'an, China
Duration: 22 Aug 202225 Aug 2022

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2022-August
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference32nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2022
Country/TerritoryChina
CityXi'an
Period22/08/2225/08/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Localization
  • deep neural networks
  • mean cyclic error
  • supervised learning
  • underwater acoustics

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