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Ambient noise imaging (ANI) uses snapping shrimp sounds to create images of underwater objects. New algorithms, developed using the ROMANIS camera, enable passive range estimation of these objects.

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Area of Science:

  • Acoustics
  • Oceanography
  • Signal Processing

Background:

  • High-frequency ambient noise in warm shallow waters is primarily generated by snapping shrimp.
  • This impulsive, broadband noise can be utilized for acoustic illumination to image submerged objects.
  • Existing ambient noise imaging (ANI) systems like ADONIS have limitations.

Purpose of the Study:

  • To develop a second-generation ANI camera, ROMANIS, to overcome ADONIS limitations.
  • To model ambient noise using statistical distributions and develop new ANI algorithms.
  • To demonstrate passive range estimation of submerged objects using ROMANIS data.

Main Methods:

  • Deployment of the ROMANIS ANI camera in field experiments.
  • Analysis of acoustic time series data to model ambient noise with symmetric α-stable (SαS) distributions.
  • Development and demonstration of ANI algorithms based on low-order moments and fractiles due to non-converging high-order moments of SαS distributions.

Main Results:

  • Ambient noise in field experiments was accurately modeled by SαS distributions.
  • Novel ANI algorithms were successfully developed and demonstrated.
  • Passive range estimation of submerged objects was achieved by localizing snaps and identifying echoes.

Conclusions:

  • The ROMANIS camera represents an advancement in ANI technology.
  • Symmetric α-stable distributions provide a suitable model for ambient noise in shallow waters.
  • The developed algorithms enable effective passive acoustic imaging and range estimation of underwater objects.