Graph-CNNs for RF Imaging: Learning the Electric Field Integral Equations
This work addresses RF imaging for scene reconstruction, offering an incremental improvement through a novel neural network design.
The paper tackles the inverse scattering problem in RF imaging by proposing a Graph-CNN architecture that learns from electric field integral equations, achieving performance gains with resilience to noise and varied reception configurations.
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.