RADIO: Parameterized generative radar data augmentation for small datasets

Marcel Sheeny, Andrew Wallace, Sen Wang

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Abstract

We present a novel, parameterised radar data augmentation (RADIO) technique to generate realistic radar samples from small datasets for the development of radar-related deep learning models. RADIO leverages the physical properties of radar signals, such as attenuation, azimuthal beam divergence and speckle noise, for data generation and augmentation. Exemplary applications on radar-based classification and detection demonstrate that RADIO can generate meaningful radar samples that effectively boost the accuracy of classification and generalisability of deep models trained with a small dataset.

Original languageEnglish
Article number3861
JournalApplied Sciences
Volume10
Issue number11
DOIs
Publication statusPublished - 2 Jun 2020

Keywords

  • Data augmentation
  • Low-THz
  • Neural networks
  • Object recognition
  • Radar

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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