Reinforced Inverse Scattering
This work addresses the challenge of optimizing imaging resources for scatterer reconstruction, which is incremental as it applies a known method (reinforcement learning) to a specific domain.
The paper tackles the problem of improving reconstruction quality in inverse wave scattering by using reinforcement learning to adaptively choose sensor positions and wave frequencies, resulting in a significant improvement with limited resources.
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.