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Tropical littoral ambient noise probability density function model based on sea surface temperature.

Piyush Asolkar1, Arnab Das2, Suhas Gajre3

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This study introduces a new model for predicting underwater ambient noise using sea surface temperature (SST) in tropical waters. This approach aids in improving sonar performance through early noise prediction.

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Area of Science:

  • Oceanography
  • Acoustics
  • Signal Processing

Background:

  • Underwater ambient noise variability in tropical shallow waters poses challenges for sonar systems.
  • Sea surface temperature (SST) influences local climate and ambient noise characteristics.

Purpose of the Study:

  • To develop an ambient noise probability density function (pdf) model for the 3-10 kHz spectral band.
  • To utilize statistical sea surface temperature (SST) distributions for noise prediction.
  • To validate the model with field data for improved sonar performance.

Main Methods:

  • Statistical analysis of SST data to build an ambient noise pdf model.
  • Model development for the 3-10 kHz frequency range.
  • Validation of the model using real-world field data.

Main Results:

  • A novel ambient noise pdf model based on SST statistical distribution was successfully designed.
  • The model demonstrates capability for early ambient noise prediction.
  • Validation confirmed the model's effectiveness in tropical shallow water environments.

Conclusions:

  • The SST-based ambient noise model offers a predictive advantage over traditional wind-based methods.
  • This model facilitates proactive mitigation strategies for enhancing sonar system performance.
  • Accurate ambient noise prediction is crucial for effective sonar operations in dynamic marine environments.