LiBeamsNet: AUV Velocity Vector Estimation in Situations of Limited DVL Beam Measurements

Nadav Cohen, Itzik Klein

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


Autonomous underwater vehicles (AUVs) are employed for marine applications and can operate in deep underwater environments beyond human reach. A standard solution for the autonomous navigation problem can be obtained by fusing the inertial navigation system and the Doppler velocity log sensor (DVL). The latter measures four beam velocities to estimate the vehicle's velocity vector. In real-world scenarios, the DVL may receive less than three beam velocities if the AUV operates in complex underwater environments. In such conditions, the vehicle's velocity vector could not be estimated leading to a navigation solution drift and in some situations the AUV is required to abort the mission and return to the surface. To circumvent such a situation, in this paper we propose a deep learning framework, LiBeamsNet, that utilizes the inertial data and the partial beam velocities to regress the missing beams in two missing beams scenarios. Once all the beams are obtained, the vehicle's velocity vector can be estimated. The approach performance was validated by sea experiments in the Mediterranean Sea. The results show up to 7.2 % speed error in the vehicle's velocity vector estimation in a scenario that otherwise could not provide an estimate.

Original languageEnglish
Title of host publicationOCEANS 2022 Hampton Roads
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665468091
StatePublished - 2022
Event2022 OCEANS Hampton Roads, OCEANS 2022 - Hampton Roads, United States
Duration: 17 Oct 202220 Oct 2022

Publication series

NameOceans Conference Record (IEEE)
ISSN (Print)0197-7385


Conference2022 OCEANS Hampton Roads, OCEANS 2022
Country/TerritoryUnited States
CityHampton Roads

Bibliographical note

Publisher Copyright:
© 2022 IEEE.


  • Autonomous underwater vehicle (AUV)
  • Deep Learning
  • Doppler velocity log (DVL)
  • Inertial navigation system (INS)

ASJC Scopus subject areas

  • Oceanography
  • Ocean Engineering


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