Graph-CNNs for RF Imaging: Learning the Electric Field Integral Equations
Journal:
arXiv
Published Date:
Mar 18, 2025
Abstract
Radio-Frequency (RF) imaging concerns the digital recreation of the surfaces
of scene objects based on the scattered field at distributed receivers. To
solve this difficult inverse scattering problems, data-driven methods are often
employed that extract patterns from similar training examples, while offering
minimal latency. In this paper, we first provide an approximate yet fast
electromagnetic model, which is based on the electric field integral equations,
for data generation, and subsequently propose a Deep Neural Network (DNN)
architecture to learn the corresponding inverse model. A graph-attention
backbone allows for the system geometry to be passed to the DNN, where residual
convolutional layers extract features about the objects, while a UNet head
performs the final image reconstruction. Our quantitative and qualitative
evaluations on two synthetic data sets of different characteristics showcase
the performance gains of thee proposed advanced architecture and its relative
resilience to signal noise levels and various reception configurations.
