1. Radar emitter multi-label recognition based on residual network
- Author
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Hao Xinhong, Liu Shao-kun, Yu Hong-hai, Yan Xiaopeng, and Li Ping
- Subjects
0209 industrial biotechnology ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Computational Mechanics ,02 engineering and technology ,Residual ,01 natural sciences ,Signal ,Convolutional neural network ,010305 fluids & plasmas ,law.invention ,020901 industrial engineering & automation ,Aliasing ,law ,0103 physical sciences ,Radar ,Common emitter ,business.industry ,Mechanical Engineering ,Metals and Alloys ,Short-time Fourier transform ,Pattern recognition ,Modulation ,Ceramics and Composites ,Artificial intelligence ,business - 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.
- Published
- 2022