Data-Driven Calibration Technique for Quantitative Inversion

Quantitative inversion algorithms allow for the reconstruction of electrical properties (such as permittivity, and conductivity) for every point in a scene. However, they are challenging to use on measured datasets due to the need to know the incident wave field in the scene. In general, this is unknown due to factors such as antenna characteristics, path loss, waveform factors, etc. In this paper, we introduce a scalar calibration factor to account for these factors. To solve for the calibration factor, we augment the inversion procedure by including the forward problem, which we solve by training a simple feed-forward fully connected neural network to learn a mapping between the underlying permittivity distribution and the scattered field at the radar. We then minimize the mismatch between the measured and simulated fields to optimize the scalar calibration factor for each transmitter. We use the Fresnel Institute dataset to test our algorithm.