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Compressive matched-field processing.

William Mantzel¹, Justin Romberg, Karim Sabra

¹School of Electrical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30308, USA. willem@gatech.edu

The Journal of the Acoustical Society of America

|July 12, 2012

Summary

This study introduces compressed sensing for matched-field processing (MFP), significantly reducing computational load. The new method accurately locates acoustic sources with substantial data compression, proving effective in ocean environments.

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Area of Science:

Acoustics
Signal Processing
Oceanography

Background:

Traditional matched-field processing (MFP) for source localization is computationally intensive due to solving numerous partial differential equations.
Existing methods require significant computational resources, limiting real-time applications and broad usability.

Purpose of the Study:

To develop a computationally efficient source localization technique by compressing calculations in matched-field processing.
To leverage compressed sensing principles to reduce the computational workload of MFP.
To demonstrate the effectiveness of the compressed MFP approach in realistic ocean environments.

Main Methods:

Constructing a low-dimensional proxy for the Green's function using backpropagation of a limited set of random receiver vectors.
Performing short correlations between the Green's function proxy and the compressed acoustic data.
Applying compressed sensing concepts to reduce the dimensionality of the MFP problem.

Main Results:

The compressed MFP approach achieves comparable source localization accuracy to traditional MFP, even with significant data compression.
Numerical experiments in a Pekeris ocean waveguide validate the effectiveness of the compressed method.
The broadband regime shows promising results, with minimal accuracy loss using only two random backpropagations per frequency.

Conclusions:

Compressed matched-field processing offers a computationally viable alternative to traditional methods for acoustic source localization.
The technique demonstrates significant computational savings with maintained accuracy, particularly in broadband scenarios.
The method's generic applicability allows for offline computation of backpropagations, enabling reuse for multiple source localization tasks.