A novel sampling method for multiple multiscale targets from scattering amplitudes at a fixed frequency

TL;DR

This paper introduces a simple, stable sampling method using scattering amplitudes for reconstructing shapes and locations of multiple multiscale targets in inverse acoustic scattering, effective even with close components.

Contribution

The paper proposes a novel sampling method based on scattering amplitudes that is easy to implement, stable, and capable of handling multiple multiscale targets, unlike classical methods.

Findings

Method involves only matrix multiplication.

Indicator functional peaks near target boundaries.

Effective for multiple close targets.

Abstract

A sampling method by using scattering amplitude is proposed for shape and location reconstruction in inverse acoustic scattering problems. Only matrix multiplication is involved in the computation, thus the novel sampling method is very easy and simple to implement. With the help of the factorization of the far field operator, we establish an inf-criterion for characterization of underlying scatterers. This result is then used to give a lower bound of the proposed indicator functional for sampling points inside the scatterers. While for the sampling points outside the scatterers, we show that the indicator functional decays like the bessel functions as the sampling point goes away from the boundary of the scatterers. We also show that the proposed indicator functional continuously dependents on the scattering amplitude, this further implies that the novel sampling method is extremely stable with respect to errors in the data. Different to the classical sampling method such as the linear sampling method or the factorization method, from the numerical point of view, the novel indicator takes its maximum near the boundary of the underlying target and decays like the bessel functions as the sampling points go away from the boundary. The numerical simulations also show that the proposed sampling method can deal with multiple multiscale case, even the different components are close to each other.