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Under-ice acoustic navigation using real-time model-aided range estimation.

EeShan C Bhatt1, Oscar Viquez1, Henrik Schmidt1

  • 1Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

The Journal of the Acoustical Society of America
|April 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces real-time sound speed prediction for long baseline (LBL) underwater navigation, improving autonomous underwater vehicle (AUV) positioning accuracy in challenging environments. The new method significantly reduces navigation errors, extending surface-level accuracy underwater.

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Area of Science:

  • Oceanography
  • Robotics
  • Acoustics

Background:

  • Underwater navigation systems like long baseline (LBL) typically assume constant sound speed, leading to inaccuracies.
  • Real-time autonomous underwater vehicle (AUV) operations require precise positioning, often challenged by variable underwater sound conditions.

Purpose of the Study:

  • To develop and validate a real-time, ray-based method for predicting effective sound speed in underwater acoustic navigation.
  • To improve the accuracy of AUV positioning using LBL systems in complex acoustic environments.

Main Methods:

  • Implemented a real-time ray-based sound speed prediction for an AUV-LBL system in the Beaufort Sea.
  • Evaluated pseudorange performance using acoustic transmissions between Global Navigation Satellite System (GNSS)-linked beacons.
  • Utilized modeled, historical, and observed sound speed profiles for AUV re-navigation.

Main Results:

  • Achieved a mean real-time absolute range error of approximately 11 meters up to 3 km between beacons.
  • Demonstrated a re-navigation accuracy of 1.84 ± 2.19 meters root mean square.
  • Identified overestimation in real-time methods, suggesting improvements for eigenray filtering.

Conclusions:

  • The developed real-time sound speed prediction method enhances LBL navigation accuracy for AUVs.
  • This approach effectively extends high-accuracy positioning from GNSS into the underwater environment.
  • The findings support more reliable autonomous operations in challenging oceanic conditions.