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Enhancing underwater target localization through proximity-driven recurrent neural networks.

Sathish Kumar1, Ravikumar Chinthaginjala1, Dhanamjayulu C1

  • 1School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, India.

Heliyon
|April 10, 2024
PubMed
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This study introduces a proximity-aware Recurrent Neural Network (RNN) for precise underwater target localization. The enhanced RNN model significantly reduces mean estimation error in challenging marine environments.

Area of Science:

  • Marine technology
  • Robotics
  • Signal processing

Background:

  • Underwater target localization is crucial for marine applications like environmental monitoring and ocean research.
  • Conventional systems struggle with signal degradation, noise, and unstable conditions in dynamic underwater environments.

Purpose of the Study:

  • To develop an accurate underwater target localization method using Recurrent Neural Networks (RNNs).
  • To enhance RNN performance by incorporating proximity-based features for improved spatial awareness.

Main Methods:

  • Utilized Recurrent Neural Networks (RNNs) to analyze temporal dynamics of audio emissions from underwater targets.
  • Integrated proximity-based features to provide relative distance information between hydrophone nodes and the target.
Keywords:
AngleDistanceLocalizationMean estimation errorRNNUWSN

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  • Evaluated the model through extensive simulations and practical experiments in diverse underwater settings.

    • Main Results:

    • The RNN-based approach demonstrated superior performance compared to conventional localization methods.
    • Significant reductions in mean estimation error (MEE) were achieved, particularly in complex underwater environments.
    • The proximity-aware RNN model proved effective even under challenging conditions.

    Conclusions:

    • The proposed proximity-aware RNN model offers a robust and accurate solution for underwater target localization.
    • This approach enhances precision by leveraging temporal data and spatial proximity information.
    • The method shows significant potential for advancing marine exploration and monitoring technologies.