Radar Signal Intrapulse Modulation Recognition Based on a Denoising-Guided Disentangled Network

oleh: Xiangli Zhang, Jiazhen Zhang, Tianze Luo, Tianye Huang, Zuping Tang, Ying Chen, Jinsheng Li, Dapeng Luo

Format: Article
Diterbitkan: MDPI AG 2022-03-01

Deskripsi

Accurate recognition of radar modulation mode helps to better estimate radar echo parameters, thereby occupying an advantageous position in the radar electronic warfare (EW). However, under low signal-to-noise ratio environments, recent deep-learning-based radar signal recognition methods often perform poorly due to the unsuitable denoising preprocess. In this paper, a denoising-guided disentangled network based on an inception structure is proposed to simultaneously complete the denoising and recognition of radar signals in an end-to-end manner. The pure radar signal representation (PSR) is disentangled from the noise signal representation (NSR) through a feature disentangler and used to learn a radar signal modulation recognizer under low-SNR environments. Signal noise mutual information loss is proposed to enlarge the gap between the PSR and the NSR. Experimental results demonstrate that our method can obtain a recognition accuracy of 98.75% in the −8 dB SNR and 89.25% in the −10 dB environment of 12 modulation formats.