Generative Adversarial Networks for Recovering Missing Spectral Information
This addresses a domain-specific issue for radar systems, but appears incremental as it applies an existing GAN method to a new data scenario.
The paper tackles the problem of missing spectral information in ultra-wideband radar systems due to shared frequency bands, proposing a generative adversarial network called SARGAN to recover this information, with initial results showing promise.
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.