基于集成时频通道注意力的倒残差神经网络干扰识别

Jamming Identification Based on Inverse Residual Neural Network with Integrated Time-frequency Channel Attention

准确识别干扰类型是实施高效抗干扰举措的先决条件。针对低干噪比(Jamming-to-Noise Ratio,JNR)的条件下干扰识别准确率低的问题,本文将信号短时傅里叶变换(Short Time Fourier Transform,STFT)后的时频图像作为卷积神经网络训练输入,提出一种以倒残差结构为主体的神经网络架构,并引入联合时频通道注意力机制模块,同时从时频图像提取时频域和通道域的综合干扰特征,充分利用多维度的干扰特征信息来准确识别干扰类型。仿真结果表明,在JNR=-8 dB时,本文所提算法能够实现对8种类型干扰100%的准确识别,在JNR=-10 dB时所有类型的干扰信号识别准确率都能达到98.3%以上,在JNR=-14 dB准确率也依然可以达到90%以上。同时分析了所提算法的网络复杂度,结果表明所提方案在时间和空间复杂度上得到了较好的折中,验证了模型的性能优越性。

Accurate identification of jamming types was a prerequisite for implementing efficient anti-jamming initiatives.Aiming at the problem of low accuracy of jamming recognition under the condition of low Jamming-to-Noise Ratio(JNR),this paper took the time-frequency images of the signal after Short-Time Fourier transform(STFT)as the training input of the convolutional neural network,proposed a neural network architecture with inverted residual structure as the main body,and introduced an attention mechanism module of joint time-frequency channel,which accurately identified the jamming type by simultaneously extracting the integrated jamming features in time-frequency and channel domains from the time-frequency images and making full use of multi-dimensional jamming feature information.The simulation results indicate that the proposed algorithm can precisely discriminate eight types of jamming signals when JNR is-8 dB.The recognition accuracy of eight types of jamming signals can reach more than 98.3%when JNR is-10 dB and can still reach more than 90%when JNR is-14 dB.The network complexity of the proposed algorithm was also analyzed.The outcomes indicate that the proposed scheme obtains a better compromise in time and space complexity,which verifies the superior performance of the model.