Generative Adversarial Networks for Recovering Missing Spectral Information
Machine Learning
2018-12-17 v2 Machine Learning
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.
Cite
@article{arxiv.1812.04744,
title = {Generative Adversarial Networks for Recovering Missing Spectral Information},
author = {Dung N. Tran and Trac D. Tran and Lam Nguyen},
journal= {arXiv preprint arXiv:1812.04744},
year = {2018}
}
Comments
arXiv admin note: text overlap with arXiv:1707.06873 by other authors