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

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).