Reinforced Inverse Scattering

TL;DR

This paper introduces a reinforcement learning approach to optimize sensor placement and wave frequencies in inverse scattering, significantly improving reconstruction quality with limited resources.

Contribution

It presents a novel reinforcement learning framework for adaptive sensor and frequency selection in inverse wave scattering problems.

Findings

Enhanced reconstruction accuracy demonstrated in numerical experiments.

Adaptive sensor placement outperforms fixed configurations.

Significant resource efficiency achieved in imaging process.

Abstract

Inverse wave scattering aims at determining the properties of an object using data on how the object scatters incoming waves. In order to collect information, sensors are put in different locations to send and receive waves from each other. The choice of sensor positions and incident wave frequencies determines the reconstruction quality of scatterer properties. This paper introduces reinforcement learning to develop precision imaging that decides sensor positions and wave frequencies adaptive to different scatterers in an intelligent way, thus obtaining a significant improvement in reconstruction quality with limited imaging resources. Extensive numerical results will be provided to demonstrate the superiority of the proposed method over existing methods.