TY - GEN
T1 - An Effective Radar Signal Recognition Method Using Neural Architecture Search
AU - Zhang, Min
AU - Luo, Wang
AU - Wang, Yu
AU - Sun, Jinlong
AU - Yang, Jie
AU - Ohtsuki, Tomoaki
N1 - Funding Information:
ACKNOWLEDGEMENT This work is supported by the open research fund of the Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology under grant KF20202106.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Radar signal recognition
KW - convolutional neural network (CNN)
KW - neural architecture search (NAS)
UR - http://www.scopus.com/inward/record.url?scp=85123016540&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123016540&partnerID=8YFLogxK
U2 - 10.1109/VTC2021-Fall52928.2021.9625235
DO - 10.1109/VTC2021-Fall52928.2021.9625235
M3 - Conference contribution
AN - SCOPUS:85123016540
T3 - IEEE Vehicular Technology Conference
BT - 2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 94th IEEE Vehicular Technology Conference, VTC 2021-Fall
Y2 - 27 September 2021 through 30 September 2021
ER -