PRI Modulation Recognition Based on Squeeze-and-Excitation Networks

被引：49

作者：

Wei, Shunjun ^{[1
]}

Qu, Qizhe ^{[1
]}

Wu, Yue ^{[1
]}

Wang, Mou ^{[1
]}

Shi, Jun ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Sichuan, Peoples R China

来源：

IEEE COMMUNICATIONS LETTERS | 2020年 / 24卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Modulation; Signal to noise ratio; Correlation; Simulation; Radar; Convolution; Mathematical model; Pulse repetition interval; modulation mode; recognition; convolution neural network;

D O I：

10.1109/LCOMM.2020.2970397

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Pulse repetition interval (PRI) modulation recognition is a significant means to analyze radar working statuses and missions in Electronic Support system. Traditional methods may be insufficient to accurately recognize complex PRI at low SNR with high percentages of missing pulses. In this letter, an approach based on Squeeze-and-Excitation networks (SE-Net) and the autocorrelation functions for PRI modulation recognition automatically is proposed. Firstly, the features of six PRI modulation types in the autocorrelation domain are converted into images by calculating instantaneous autocorrelation functions. Then, the images will be the input of SE-Net which will automatically learn about deep features of different PRI modulation modes. Finally, SE-Net will output PRI modulation modes directly. Simulation results show that SE-Net is robust to the noise and missing pulses. The accuracies for all PRI modulation modes are more than 95% at -10dB with 30% missing pulses and more than 96% at -2dB with 50% missing pulses. Compared with traditional method and other networks, SE-Net can achieve better recognition performance at low SNR and high percentages of missing pulses.

引用

页码：1047 / 1051

页数：5

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