Intra-pulse modulation radar signal recognition based on CLDN network

• Published in: IET radar, sonar & navigation Vol. 14; no. 6; pp. 803 - 810
• Main Authors: Wei, Shunjun, Qu, Qizhe, Su, Hao, Wang, Mou, Shi, Jun, Hao, Xiaojun
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.06.2020
• Subjects: autocorrelation domain; automatic modulation classification; civilian applications; CLDN network; convolutional neural nets; deep learning network; deep neural network; feature extraction; frequency domain; intra‐pulse modulation radar signal recognition; learning (artificial intelligence); LSTM network; military applications; modulation; pulse modulation; radar signal processing; radar signals; raw signal sequences; recognition methods; recognition performances; recurrent neural nets; Research Article; shallow CNN; shallow convolution neural network; short‐term memory network; signal classification; signal modulation types; signal‐to‐noise ratio; time domain; raw signal sequences; time domain; modulation; signal modulation types; CLDN network; pulse modulation; frequency domain; radar signal processing; shallow CNN; intra-pulse modulation radar signal recognition; autocorrelation domain; feature extraction; LSTM network; radar signals; shallow convolution neural network; learning (artificial intelligence); recognition methods; deep neural network; short-term memory network; recurrent neural nets; military applications; automatic modulation classification; signal classification; civilian applications; signal-to-noise ratio; convolutional neural nets; recognition performances; deep learning network

Automatic modulation classification of radar signals, which plays a significant role in both civilian and military applications, is researched in this study through a deep learning network. In this study, a novel network combined a shallow convolution neural network (CNN), long short-term memory (LS...