BeamsNet: A data-driven approach enhancing Doppler velocity log measurements for autonomous underwater vehicle navigation

Nadav Cohen, Itzik Klein

Research output: Contribution to journalArticlepeer-review


Autonomous underwater vehicles (AUV) perform various applications such as seafloor mapping and underwater structure health monitoring. Commonly, an inertial navigation system aided by a Doppler velocity log (DVL) is used to provide the vehicle's navigation solution. In such fusion, the DVL provides the velocity vector of the AUV, which determines the navigation solution's accuracy and helps estimate the navigation states. This paper proposes BeamsNet, an end-to-end deep learning framework to regress the estimated DVL velocity vector that improves the accuracy of the velocity vector estimate, and could replace the model-based approach. Two versions of BeamsNet, differing in their input to the network, are suggested. The first uses the current DVL beam measurements and inertial sensor data, while the other utilizes only DVL data, taking the current and past DVL measurements for the regression process. Both simulation and sea experiments were made to validate the proposed learning approach relative to the model-based approach. Sea experiments were made with the Snapir AUV in the Mediterranean Sea, collecting approximately four and a half hours of DVL and inertial sensor data. Our results show that the proposed approach achieved an improvement of more than 60% in estimating the DVL velocity vector.

Original languageEnglish
Article number105216
JournalEngineering Applications of Artificial Intelligence
StatePublished - Sep 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s)


  • Autonomous underwater vehicle (AUV)
  • Deep learning
  • Doppler velocity log (DVL)
  • Inertial measurement unit (IMU)
  • Inertial navigation system (INS)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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