A Scalable Deep Learning Model for Simultaneous Reconstruction and Transmitter Localization in Inverse Scattering

There have been several methods for solving the problem of inverse scattering. Recently, Deep Learning methods have been able to provide state-of-the-art results in inverse scattering. However, both traditional and Deep Learning based methods require the knowledge of the locations of the transmitters and receivers. This requires a calibration stage which involves the careful placement of the transmitters and receivers at specific known locations or placing the transmitters and receivers at arbitrary locations and using a system to calculate their respective positions. This reduces the ease of usability of the system. Therefore, in this work, we propose a Deep Learning based approach which can be used to simultaneously reconstruct the contrast and localize the transmitters.