Radar signal recognition based on time-frequency feature extraction and convolutional neural network

Xinjie Ju; Hang Zhu; Guning Wang; Xiaojun Zou; Ming Tan; Wei Song

doi:10.1117/12.2673527

3 April 2023 Radar signal recognition based on time-frequency feature extraction and convolutional neural network

Xinjie Ju, Hang Zhu, Guning Wang, Xiaojun Zou, Ming Tan, Wei Song

Proceedings Volume 12599, Second International Conference on Digital Society and Intelligent Systems (DSInS 2022); 1259929 (2023) https://doi.org/10.1117/12.2673527
Event: 2nd International Conference on Digital Society and Intelligent Systems (DSInS 2022), 2022, Chendgu, China

Abstract

To solve the problem of difficult feature extraction and low recognition rate of radar signal under low signal-to-noise ratio, this paper proposes a radar signal recognition method based on time-frequency feature extraction and convolutional neural network. This method uses short-term Fourier transform (STFT) to obtain two-dimensional time-frequency images of radar signals, and then sends the images to convolutional neural networks for deep feature extraction, and realizes the classification and recognition of radar signals through convolutional neural network classifiers. The simulation results show that for different intra-pulse modulated radar signals, when the signal-to-noise ratio is -5dB, the overall recognition accuracy of the proposed model can reach more than 93%, which effectively solves the problem of low radar signal recognition rate under low signal-to-noise ratio.

Citation Download Citation

Xinjie Ju, Hang Zhu, Guning Wang, Xiaojun Zou, Ming Tan, and Wei Song "Radar signal recognition based on time-frequency feature extraction and convolutional neural network", Proc. SPIE 12599, Second International Conference on Digital Society and Intelligent Systems (DSInS 2022), 1259929 (3 April 2023); https://doi.org/10.1117/12.2673527

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available