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http://dx.doi.org/10.4218/etrij.2020-0338

Automatic modulation classification of noise-like radar intrapulse signals using cascade classifier  

Meng, Xianpeng (Department of Electronic and Optical Engineering, Army Engineering University)
Shang, Chaoxuan (Department of Electronic and Optical Engineering, Army Engineering University)
Dong, Jian (Department of Electronic and Optical Engineering, Army Engineering University)
Fu, Xiongjun (School of Information and Electronics, Beijing Institute of Technology)
Lang, Ping (School of Information and Electronics, Beijing Institute of Technology)
Publication Information
ETRI Journal / v.43, no.6, 2021 , pp. 991-1003 More about this Journal
Abstract
Automatic modulation classification is essential in radar emitter identification. We propose a cascade classifier by combining a support vector machine (SVM) and convolutional neural network (CNN), considering that noise might be taken as radar signals. First, the SVM distinguishes noise signals by the main ridge slice feature of signals. Second, the complex envelope features of the predicted radar signals are extracted and placed into a designed CNN, where a modulation classification task is performed. Simulation results show that the SVM-CNN can effectively distinguish radar signals from noise. The overall probability of successful recognition (PSR) of modulation is 98.52% at 20 dB and 82.27% at -2 dB with low computation costs. Furthermore, we found that the accuracy of intermediate frequency estimation significantly affects the PSR. This study shows the possibility of training a classifier using complex envelope features. What the proposed CNN has learned can be interpreted as an equivalent matched filter consisting of a series of small filters that can provide different responses determined by envelope features.
Keywords
complex envelope; convolutional neural network; modulation classification; radar emitter identification;
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