An Effective Radar Signal Recognition Method Using Neural Architecture Search

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665413688
DOI
出版ステータスPublished - 2021
イベント94th IEEE Vehicular Technology Conference, VTC 2021-Fall - Virtual, Online, United States
継続期間: 2021 9月 272021 9月 30

出版物シリーズ

名前IEEE Vehicular Technology Conference
2021-September
ISSN(印刷版)1550-2252

Conference

Conference94th IEEE Vehicular Technology Conference, VTC 2021-Fall
国/地域United States
CityVirtual, Online
Period21/9/2721/9/30

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 電子工学および電気工学
  • 応用数学

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