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- Title
Radar emitter multi-label recognition based on residual network.
- Authors
Yu Hong-hai; Yan Xiao-peng; Liu Shao-kun; Li Ping; Hao Xin-hong
- Abstract
In low signal-to-noise ratio (SNR) environments, the traditional radar emitter recognition (RER) method struggles to recognize multiple radar emitter signals in parallel. This paper proposes a multi-label classification and recognition method for multiple radar-emitter modulation types based on a residual network. This method can quickly perform parallel classification and recognition of multi-modulation radar time-domain aliasing signals under low SNRs. First, we perform time-frequency analysis on the received signal to extract the normalized time-frequency image through the short-time Fourier transform (STFT). The time-frequency distribution image is then denoised using a deep normalized convolutional neural network (DNCNN). Secondly, the multi-label classification and recognition model for multi-modulation radar emitter time-domain aliasing signals is established, and learning the characteristics of radar signal time-frequency distribution image dataset to achieve the purpose of training model. Finally, time-frequency image is recognized and classified through the model, thus completing the automatic classification and recognition of the time-domain aliasing signal. Simulation results show that the proposed method can classify and recognize radar emitter signals of different modulation types in parallel under low SNRs.
- Subjects
SIGNAL denoising; SIGNAL-to-noise ratio; SIGNAL processing; IMAGE processing; FOURIER transforms
- Publication
Defence Technology, 2022, Vol 18, Issue 3, p410
- ISSN
2096-3459
- Publication type
Article
- DOI
10.1016/j.dt.2021.02.005