基于时频特征的跳频信号调制识别  

Frequency-hopping signal modulation recognition based on time-frequency features

作　　者：张静[1] 于蕾[1] 侯长波[1] 张结 林佳昕 ZHANG Jing;YU Lei;HOU Changbo;ZHANG Jie;LIN Jiaxin(College of Information and Communication Engineering,Harbin Engineering University,Harbin Heilongjiang 150001,China)

机构地区：[1]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001

出　　处：《太赫兹科学与电子信息学报》2022年第1期40-46,共7页Journal of Terahertz Science and Electronic Information Technology

基　　金：国家自然科学基金资助项目(62001137)。

摘　　要：跳频信号在抗干扰方面具有良好的性能。准确识别跳频信号的调制方式,能够为判断敌我目标属性、干扰敌方信号等军事信息战提供有力支撑,但国内外对于跳频信号的调制识别仍存在很大空缺。本文提出一种基于时频特征的跳频信号调制识别方法,通过平滑伪魏格纳-维利分布(SPWVD)时频变换获取不同调制类型的跳频信号时频图像,将时频图像送入卷积神经网络(CNN)中进行特征提取及分类识别。仿真实验证明,本文CNN在低信噪比下取得了较好的识别效果。Frequency-hopping signal shows good performance in anti-interference. Accurately identifying the modulation methods of frequency-hopping signals can provide strong support for military information warfare such as judging the attributes of enemy and enemy targets and interfering with enemy signals. Nevertheless, there is still a big gap in the modulation recognition of frequency hopping signals at home and abroad. A frequency-hopping signal modulation recognition method based on time-frequency features is proposed. Through Smoothed Pseudo Wigner-Ville Distribution(SPWVD) time-frequency transformation, time-frequency images of frequency-hopping signals of different modulation types are obtained, and the time-frequency images are sent to a Convolutional Neural Network(CNN) for feature extraction and classification recognition. Simulation experiments prove that the proposed CNN model has achieved better recognition results under low Signal-to-Noise Ratios(SNRs).

关 键 词：跳频信号 调制识别 时频分析 卷积神经网络 特征提取 

分 类 号：TN913.3[电子电信—通信与信息系统]