The linear sampling method for random sources

TL;DR

This paper extends the linear sampling method to inverse acoustic scattering with random point sources, providing theoretical analysis and numerical validation of its robustness and accuracy.

Contribution

It introduces a novel extension of the linear sampling method for random sources, supported by theoretical analysis and practical MATLAB implementations.

Findings

The method effectively reconstructs sources in numerical experiments.

The approach is robust against measurement noise.

Theoretical justification confirms the method's validity.

Abstract

We present an extension of the linear sampling method for solving the sound-soft inverse acoustic scattering problem with randomly distributed point sources. The theoretical justification of our sampling method is based on the Helmholtz--Kirchhoff identity, the cross-correlation between measurements, and the volume and imaginary near-field operators, which we introduce and analyze. Implementations in MATLAB using boundary elements, the SVD, Tikhonov regularization, and Morozov's discrepancy principle are also discussed. We demonstrate the robustness and accuracy of our algorithms with several numerical experiments in two dimensions.