Compressive Matched-Field Processing

TL;DR

This paper presents a compressed sensing approach to source localization in matched-field processing, significantly reducing computational effort while maintaining accuracy, especially in broadband scenarios.

Contribution

It introduces a novel compressed sensing method for MFP that constructs a low-dimensional proxy for the Green's function using random backpropagations, enabling efficient source localization.

Findings

Compressed MFP performs as well as traditional MFP with significant compression.

Using two random backpropagations per frequency yields near-equivalent results to full broadband MFP.

The method allows offline computation of backpropagations, enhancing efficiency and reusability.

Abstract

Source localization by matched-field processing (MFP) generally involves solving a number of computationally intensive partial differential equations. This paper introduces a technique that mitigates this computational workload by "compressing" these computations. Drawing on key concepts from the recently developed field of compressed sensing, it shows how a low-dimensional proxy for the Green's function can be constructed by backpropagating a small set of random receiver vectors. Then, the source can be located by performing a number of "short" correlations between this proxy and the projection of the recorded acoustic data in the compressed space. Numerical experiments in a Pekeris ocean waveguide are presented which demonstrate that this compressed version of MFP is as effective as traditional MFP even when the compression is significant. The results are particularly promising in the broadband regime where using as few as two random backpropagations per frequency performs almost as well as the traditional broadband MFP, but with the added benefit of generic applicability. That is, the computationally intensive backpropagations may be computed offline independently from the received signals, and may be reused to locate any source within the search grid area.