电讯技术2023，Vol.63Issue(12)：1862-1868,7.DOI:10.20079/j.issn.1001-893x.230814003
一种基于改进残差神经网络的直扩信号感知方法
A DSSS Signal Sensing Method Based on Improved Residual Neural Networks
任江飞 1许魁 1刘洋 1陆瑞 1张咪 1叶子绿1
作者信息
- 1. 陆军工程大学 通信工程学院,南京 210007
- 折叠
摘要
Abstract
The problem of direct sequence spreading spectrum(DSSS)signal sensing under non-cooperative conditions has always been a hot research topic in the field of communication countermeasures,and the performance of traditional sensing methods is seriously degraded under low signal to noise ratio(SNR)conditions.In order to effectively improve the performance of DSSS signal sensing,a DSSS signal sensing method based on an improved residual neural network(ResNet)is proposed.Firstly,the correlation peak characteristics of the DSSS signal are quickly extracted by the Generalized Cross Correlation(GCC)algorithm.Then,based on the ResNet,the attention mechanism is introduced to build a network model to analyze and identify the abstract features.Finally,experimental validation is carried out in the simulated dataset.The results show that,compared with the time-domain autocorrelation method and the convolutional neural network method,the proposed method has a better sensing effect and can effectively sense the DSSS signals with-16 dB SNR.关键词
直扩信号感知/广义互相关/残差神经网络/注意力机制Key words
DSSS signal sensing/generalized cross correlation/residual neural network/attention mechanism分类
信息技术与安全科学引用本文复制引用
任江飞,许魁,刘洋,陆瑞,张咪,叶子绿..一种基于改进残差神经网络的直扩信号感知方法[J].电讯技术,2023,63(12):1862-1868,7.
