摘要
为解决低信噪比条件下相移键控和正交幅度调制类信号利用时频图像分类时识别率低的问题,提出一种信号特征融合的方法。首先对接收信号数据进行高阶累积量计算,获取一维数值特征向量;其次采用时频分析方法预处理得到信号时频图,利用卷积神经网络提取其一维图像特征向量;将两类特征向量级联得到一维融合特征向量,基于融合后的特征向量经过全连接网络进一步运算后得出分类识别结果。仿真结果显示,在1 dB条件下,相比于单一图像特征,采用特征融合的方法可将调制信号的识别准确率提高10%~30%。
In order to solve the problem of low recognition rate of phase shift keying and quadrature amplitude modulation signals when using time-frequency image classification under the condition of low signal-to-noise ratio,this paper proposes a method of signal feature fusion.Firstly,the method calculates the high-order cumulant of the received signal and obtains the one-dimensional numerical eigenvector.Then,the time-frequency diagram of the received signal is obtained by time-frequency analysis,and the one-dimensional image feature vector is extracted by convolution neural network.The two kinds of feature vectors are connected to obtain one-dimensional fusion feature vector.Finally,the fused feature vector is input into the full connection layer and the classification results are output.The simulation results show that under the condition of about 1 dB,the recognition rate of phase shift keying and quadrature amplitude modulation signals can be improved by about 10%~30% compared with the method of single image feature.
作者
钱磊
吴昊
张涛
张江
Qian Lei;Wu Hao;Zhang Tao;Zhang Jiang(The 63rd Research Institute of National University of Defense Technology,Nanjing 210007,China;School of Electronic Science,National University of Defense Technology,Changsha 410073,China)
出处
《电子技术应用》
2022年第11期89-93,共5页
Application of Electronic Technique
基金
军委科技委基础加强计划技术领域基金项目(2019-JCJQ-JJ-221)。
关键词
调制识别
高阶累积量
时频分析
特征融合
modulation recognition
high-order cumulant
time-frequency analysis
feature fusion
