# Generative Adversarial Networks for Recovering Missing Spectral Information

## TL;DR

This paper introduces SARGAN, a generative adversarial network designed to recover missing spectral information in UWB radar signals caused by spectrum sharing, demonstrating promising initial results.

## Contribution

The paper presents a novel GAN architecture, SARGAN, specifically tailored for recovering missing spectral data in radar signals, advancing spectral reconstruction techniques.

## Key findings

- SARGAN effectively learns the relationship between original and missing spectral bands.
- Initial results show promising recovery of missing spectral information.
- The approach outperforms traditional methods in spectral reconstruction tasks.

## Abstract

Ultra-wideband (UWB) radar systems nowadays typical operate in the low frequency spectrum to achieve penetration capability. However, this spectrum is also shared by many others communication systems, which causes missing information in the frequency bands. To recover this missing spectral information, we propose a generative adversarial network, called SARGAN, that learns the relationship between original and missing band signals by observing these training pairs in a clever way. Initial results shows that this approach is promising in tackling this challenging missing band problem.