Spectral Gap Extrapolation and Radio Frequency Interference Suppression Using 1D UNets

Arun Asokan Nair, Akshay Rangamani, Lam H. Nguyen, Muyinatu A.Lediju Bell, Trac D. Tran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Modern ultra-wideband (UWB) radar systems transmit a wide range of frequencies, spanning hundreds of MHz to a few GHz, to achieve improved penetration depth and narrower pulse width. A common challenge faced is the presence of other commercial transmission equipment operating in the same band, causing radio frequency interference (RFI). To overcome this RFI issue, radar systems have been developed to either avoid operating in bands with RFI or suppress the RFI after reception. In this work, we examine both families of operation and demonstrate that 1D convolutional neural networks based on the UNet architecture can provide powerful signal enhancement capabilities on raw UWB radar data. The model is trained purely on simulated data and translated to real UWB data, achieving impressive results compared to traditional sparse-recovery baseline algorithms.

Original languageEnglish (US)
Title of host publication2021 IEEE Radar Conference
Subtitle of host publicationRadar on the Move, RadarConf 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728176093
DOIs
StatePublished - May 7 2021
Externally publishedYes
Event2021 IEEE Radar Conference, RadarConf 2021 - Atlanta, United States
Duration: May 8 2021May 14 2021

Publication series

NameIEEE National Radar Conference - Proceedings
Volume2021-May
ISSN (Print)1097-5659

Conference

Conference2021 IEEE Radar Conference, RadarConf 2021
Country/TerritoryUnited States
CityAtlanta
Period5/8/215/14/21

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • convolutional neural network
  • radio frequency interference suppression
  • Spectral gap extrapolation
  • ultra-wideband radar

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