Generative Adversarial Networks for Recovering Missing Spectral Information

Autor:	Tran, Dung N., Tran, Trac D., Nguyen, Lam
Rok vydání:	2018
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: text overlap with arXiv:1707.06873 by other authors
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1812.04744 Zobrazit plný text záznamu View this record from Arxiv