Generative Adversarial Networks for Recovering Missing Spectral Information

2018/12/11 by Dũng Trần, Dung N. Tran, Trac D. Tran +4
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Geophysical Methods and Applications #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Microwave Imaging and Scattering Analysis #Ultra-Wideband Communications Technology #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1812.04744

arXiv admin note: text overlap with arXiv:1707.06873 by other authors

openalex publication_date 2018/12/11 · arxiv created 2018/12/13 · arxiv updated 2018/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
• Semantic Image Synthesis via Adversarial Learning

Related

• BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial Network
• Generating a Robust Dataset for Radar Detection in the CBRS Band
• Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing
• Waves of Imagination: Unconditional Spectrogram Generation using Diffusion Architectures
• Automatic Radar Signal Detection and FFT Estimation using Deep Learning