• ETRI Journal
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• v.43 no.6
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• pp.991-1003
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• 2021

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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.7
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• pp.1536-1541
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• 2009

In this paper, we present a novel pulse train identification method for two step stagger pulse train. Generally radar uses a fixed pulse train, and it is easy for electronic warfare system to measure the pulse repeat interval(PRI) and identify the radar. But it is very difficult to measure the PRI of stagger pulse radar because the pulse interval is periodically changed. We suggest a novel method to measure the PRI and identify the radars using the second deviation of pulse train. This method is faster comparing with Histogram method. We have a good PRI measurement results for 2 step stagger signals.