基于SPWVD和改进AlexNet的复合干扰识别
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南京航空航天大学电子信息工程学院,南京 211100

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航空科学基金(2017052015,20182007001)资助项目。


Composite Jamming Recognition Based on SPWVD and Improved AlexNet
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School of Electronical Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100,China

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    摘要:

    针对现代电子战电磁环境复杂,复合干扰信号有效特征难提取,识别难度大的问题,提出了一种基于伪平滑魏格纳-威利分布(Smoothed pseudo Wigner-Ville distribution,SPWVD)和改进AlexNet的复合干扰识别算法。该算法利用SPWVD对复合干扰信号进行时频分析,再利用图像处理技术对时频特征进行降维,最后结合改进的AlexNet模型,采用多个小的卷积核替代大的卷积核,删除全连接层7和局部响应归一化模块等手段,来减小网络参数从而加快计算速度,完成复合干扰信号的识别。仿真结果表明,在干(信)噪比为0 dB时,目标信号和6种复合干扰信号的识别率均在90%以上。与AlexNet模型相比,改进后的网络在识别准确率上有明显提高。

    Abstract:

    In view of the complex electromagnetic environment of modern electronic warfare, it is difficult to extract the effective features of composite jamming signals and identify them. In this paper, a combined jamming recognition algorithm based on smoothed pseudo Wigner-Ville distribution (SPWVD) and improved AlexNet is proposed. The algorithm uses the SPWVD to analyze the time and frequency of the composite jamming signal. Then the image processing technology is used to reduce the dimension of time-frequency characteristics. Finally, combined with the improved AlexNet model, the algorithm uses several small convolution kernels to replace the large convolution kernel, removes the full-connection layer 7 and the local response normalization module to reduce the network parameters and speed up the calculation, so as to realize the recognition of composite jamming signals. Simulation results show that when the jamming(signal) to noise ratio is 0 dB, the recognition rates of the target signal and six kinds of composite jamming signals are all above 90%. Compared with the AlexNet model, the improved network has significant improvement in recognition accuracy.

    表 3 实验参数Table 3 Experimental parameters
    表 1 Conv 1和Conv 2的具体参数Table 1 Specific parameters of Conv 1 and Conv 2
    图1 两个信号叠加的时频图Fig.1 Time-frequency diagram of two superimposed signals
    图2 目标信号和复合干扰信号的SPWVD时频图Fig.2 SPWVD time-frequency diagrams of target signal and composite jamming signal
    图3 复合干扰特征降维流程图Fig.3 Flow chart of composite jamming feature dimensionality reduction
    图4 AlexNet网络结构Fig.4 Structure of AlexNet network
    图5 改进的AlexNet网络结构Fig.5 Structure of the improved AlexNet network
    图6 识别算法流程Fig.6 Flow chart of recognition algorithm
    图7 训练集损失及准确率随迭代次数变化曲线Fig.7 Change curve of training set loss and accuracy rate versus the iteration number
    图8 测试集准确率随迭代次数变化曲线Fig.8 Change curve of test set accuracy versus the iteration number
    图9 复合干扰信号识别率曲线Fig.9 Composite jamming signal recognition rate curve
    图10 AlexNet网络和本文网络识别率比较Fig.10 Recognition rate comparison between AlexNet network and this article network
    图11 干(信)噪比为6 dB的混淆矩阵Fig.11 Confusion matrix with jamming(signal) to noise ratio of 6 dB
    图12 识别率随波门拖引信号与噪声调制信号功率比的变化曲线Fig.12 Change curve of recognition rate versus the power ratio of gate pull off signal to noise modulation signal
    图13 5种算法识别率比较Fig.13 Recognition rate comparison of five algorithms
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尚东东,张劲东,杜盈,尹明月.基于SPWVD和改进AlexNet的复合干扰识别[J].数据采集与处理,2021,36(3):577-586

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  • 收稿日期:2020-06-10
  • 最后修改日期:2021-01-06
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  • 在线发布日期: 2021-06-16