An Effective Radar Signal Recognition Method Using Neural Architecture Search

Min Zhang, Wang Luo, Yu Wang, Jinlong Sun, Jie Yang, Tomoaki Ohtsuki

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


Deep learning-based radar signal recognition is considered one of the important technologies in the field of electronic countermeasure (ECM). However, existing deep learning-based methods require much time to design a specific neural network by experts for recognizing radar signals. It is difficult to employ these methods in real application scenarios. To solve this problem, we proposed an effective radar signal recognition method using neural architecture search (NAS) to automatically design convolutional neural networks (CNN). Experiments are given to validate the proposed method via comparing with both machine learning and deep learning-based methods. Experimental results show that the proposed method can achieve the optimal accuracy with low parameters and floating-point operations.

Original languageEnglish
Title of host publication2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665413688
Publication statusPublished - 2021
Event94th IEEE Vehicular Technology Conference, VTC 2021-Fall - Virtual, Online, United States
Duration: 2021 Sept 272021 Sept 30

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference94th IEEE Vehicular Technology Conference, VTC 2021-Fall
Country/TerritoryUnited States
CityVirtual, Online


  • Radar signal recognition
  • convolutional neural network (CNN)
  • neural architecture search (NAS)

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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