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

非合作条件下直扩信号感知问题一直是通信对抗领域研究的热点,传统的感知方法在低信噪比条件下性能下降严重。为有效提升直扩信号感知的性能,提出了一种基于改进残差神经网络的直扩信号感知方法。首先,通过广义互相关算法,快速提取直扩信号的相关峰特性;然后,以残差神经网络为基础,融合注意力机制,建立网络模型,分析识别抽象特征;最后,在仿真的数据集中进行实验验证。结果表明,相较于时域自相关法、卷积神经网络法等,所提方法具备更好的感知效果,能够在信噪比为-16 dB的情形下有效地感知直扩信号。

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.