# Graph-CNNs for RF Imaging: Learning the Electric Field Integral Equations

arXiv: https://arxiv.org/abs/2503.14439 · HTML: https://mlsanity.com/paper/2503.14439/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Physics Embedded Machine Learning for Electromagnetic Data Imaging](https://mlsanity.com/paper/2207.12607/) (2022)
- [Towards Distributed and Intelligent Integrated Sensing and Communications for 6G Networks](https://mlsanity.com/paper/2402.11630/) (2024)

## Built on by (most cited first, 3 of 3)

- [A Physics-Informed Hierarchical Neural Network for Microwave Scattering Analysis of 3D PEC Targets](https://mlsanity.com/paper/2508.03774/) (2025)
- [Learning the Basis: A Kolmogorov-Arnold Network Approach Embedding Green's Function Priors](https://mlsanity.com/paper/2511.08655/) (2025)
- [Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter](https://mlsanity.com/paper/2606.28441/) (2026)

## Abstract (arXiv)

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.