Open AccessImproving Robustness and Defense with Signal Augmentation for Modulation Identification
Author(s) -
Chuanjin Zou,
Chenqiu Yu,
Jun Guo,
Ronghai Guo,
Junzhong Pang,
Weijun Zeng
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1616/1/012111
Subject(s) - robustness (evolution) , computer science , electronic warfare , modulation (music) , artificial neural network , convolutional neural network , identification (biology) , artificial intelligence , key (lock) , signal (programming language) , speech recognition , pattern recognition (psychology) , machine learning , computer security , telecommunications , radar , biochemistry , chemistry , philosophy , botany , biology , gene , programming language , aesthetics
Based on the Convolutional Neural Network, we consider finding an automatic recognition method of signal modulation. Electronic warfare (EW) is one of the most important forms of war, and the outcome of the war depends largely on it . Fast, accurate, secure and continuous communication is increasingly important, and the identification of signal modulation is one of the key links to ensure communication. The traditional recognition method is artificial recognition, which is inefficient and error-prone. In this paper, aiming at several common modulation signals, the automatic recognition system of modulation signals based on Convolu-tional Neural Network is designed by analyzing mathematical models and important parameters, then upgrade and improve the feasibility and effectiveness of the project.