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Target localization through a data-based sensitivity kernel: a perturbation approach applied to a multistatic

Selda Yildiz1, Philippe Roux2, Sandrine T Rakotonarivo3

  • 1Marine Physical Laboratory, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California 92093-0238.

The Journal of the Acoustical Society of America
|September 20, 2014
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Summary
This summary is machine-generated.

This study introduces a novel method for target localization by isolating forward scattered fields. The approach utilizes a data-based sensitivity kernel and is experimentally validated for enhanced detection in complex environments.

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Area of Science:

  • Acoustics
  • Wave Propagation
  • Inverse Problems

Background:

  • Target localization in complex environments is challenging due to signal scattering and reverberation.
  • Distinguishing forward scattered fields from incident fields is crucial for accurate localization.
  • Existing methods may struggle with sparse data or complex acoustic scenarios.

Purpose of the Study:

  • To develop and validate a method for isolating the forward scattered field from the incident field for object localization.
  • To utilize a finite-frequency perturbation approach and a data-based sensitivity kernel for this purpose.
  • To demonstrate the effectiveness of the method in a complex acoustic environment with experimental validation.

Main Methods:

  • Development of a method based on finite-frequency perturbation theory.
  • Measurement and construction of a data-based sensitivity kernel from field data.
  • Experimental validation using a cylindrical tank and ping-pong balls in a multistatic acoustic configuration.
  • Localization using direct-path arrivals and analysis of benefits from reverberation.

Main Results:

  • Successful isolation of the forward scattered field from the incident field.
  • Experimental confirmation of the method's efficacy in localizing targets.
  • Demonstration of target localization using sparse sensitivity kernel data.
  • Observation that including reverberation enhances localization accuracy.

Conclusions:

  • The developed method effectively isolates forward scattered fields for target localization in complex environments.
  • The use of a data-based sensitivity kernel provides a robust approach for localization.
  • Reverberation can be beneficially incorporated to improve localization performance.