The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-n...The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.展开更多
Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the ...Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.展开更多
To meet the actual requirement of automatic monitoring of the shortwave signals under wide band ranges, a technique for automatic recognition is studied in this paper. And basing upon the spectrum and modulation chara...To meet the actual requirement of automatic monitoring of the shortwave signals under wide band ranges, a technique for automatic recognition is studied in this paper. And basing upon the spectrum and modulation characters of amplitude modulation （AM） signals, an automatic recognition scheme for AM signals is proposed. The proposed scheme is achieved by a joint judgment with four different characteristic parameters. Experiment results indicate that the proposed scheme can effectively recognize AM signals in practice.展开更多
In view of low recognition rate of complex radar intra-pulse modulation signal type by traditional methods under low signal-to-noise ratio(SNR),the paper proposes an automatic recog-nition method of complex radar intr...In view of low recognition rate of complex radar intra-pulse modulation signal type by traditional methods under low signal-to-noise ratio(SNR),the paper proposes an automatic recog-nition method of complex radar intra-pulse modulation signal type based on deep residual network.The basic principle of the recognition method is to obtain the transformation relationship between the time and frequency of complex radar intra-pulse modulation signal through short-time Fourier transform(STFT),and then design an appropriate deep residual network to extract the features of the time-frequency map and complete a variety of complex intra-pulse modulation signal type recognition.In addition,in order to improve the generalization ability of the proposed method,label smoothing and L2 regularization are introduced.The simulation results show that the proposed method has a recognition accuracy of more than 95%for complex radar intra-pulse modulation sig-nal types under low SNR(2 dB).展开更多
针对神经网络提取的信号特征不足导致信号识别率下降的问题,提出基于门控注意力网络的调制信号分类识别算法。该算法先对输入信号进行混合数据增强,生成更多维度的样本以便网络更好地提取信号特征;再将处理后的样本信号输入双通道网络(C...针对神经网络提取的信号特征不足导致信号识别率下降的问题,提出基于门控注意力网络的调制信号分类识别算法。该算法先对输入信号进行混合数据增强,生成更多维度的样本以便网络更好地提取信号特征;再将处理后的样本信号输入双通道网络(CNN and BiLSTM Parallel),并行提取信号的空间特征和时间特征;最后将提取到的特征输入到门控注意力网络中,自适应地调整特征权重,减少网络复杂度。实验表明,文中提出的算法最高分类准确率为92.3%,优于对比的其他网络模型。展开更多
自动调制识别是现代通信系统中一项重要技术。为提高通信系统对不同调制信号间的识别性能,文中首先探索了包含11类调制信号的公开数据集RML2016.10A上原始同相正交(In-phase and Quadrature,IQ)格式数据和经过数据预处理后的幅度和相位(...自动调制识别是现代通信系统中一项重要技术。为提高通信系统对不同调制信号间的识别性能,文中首先探索了包含11类调制信号的公开数据集RML2016.10A上原始同相正交(In-phase and Quadrature,IQ)格式数据和经过数据预处理后的幅度和相位(Amplitude and Phase,AP)格式数据的差异;随后,依据原始IQ格式数据和AP格式数据在特征提取过程中对局部相关性及时序特征敏感性的差异,设计了针对空间特征提取的SFE-Block模块、针对长期依赖关系提取的TFE-Block模块,以及联合时空特征提取模块STFE-Block,并将前两者的输出特征作为STFE-Block模块输出特征的重要补充进行特征融合,以全连接(Fully Connected)层负责最终分类。实验结果表明,本模型在数据集RML2016.10A上表现良好。当信噪比(Signal to Noise Ratio,SNR)低于-8 dB时,平均识别精度比其他模型提升7%,而SNR在0~18 dB时,平均识别精度比其他模型提高1%~8%,且在SNR为16 dB时,最高识别精度达92.95%。此外,在RML2016.10B数据集上重复了实验以检验模型泛化性,所得结果同样最优,且当SNR为12 dB时,最高识别精度达到93.6%。展开更多
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation 2022M720419 to provide fund for conducting experiments。
文摘The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation under Grant No.2022M720419。
文摘Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.
文摘To meet the actual requirement of automatic monitoring of the shortwave signals under wide band ranges, a technique for automatic recognition is studied in this paper. And basing upon the spectrum and modulation characters of amplitude modulation （AM） signals, an automatic recognition scheme for AM signals is proposed. The proposed scheme is achieved by a joint judgment with four different characteristic parameters. Experiment results indicate that the proposed scheme can effectively recognize AM signals in practice.
文摘In view of low recognition rate of complex radar intra-pulse modulation signal type by traditional methods under low signal-to-noise ratio(SNR),the paper proposes an automatic recog-nition method of complex radar intra-pulse modulation signal type based on deep residual network.The basic principle of the recognition method is to obtain the transformation relationship between the time and frequency of complex radar intra-pulse modulation signal through short-time Fourier transform(STFT),and then design an appropriate deep residual network to extract the features of the time-frequency map and complete a variety of complex intra-pulse modulation signal type recognition.In addition,in order to improve the generalization ability of the proposed method,label smoothing and L2 regularization are introduced.The simulation results show that the proposed method has a recognition accuracy of more than 95%for complex radar intra-pulse modulation sig-nal types under low SNR(2 dB).
文摘针对神经网络提取的信号特征不足导致信号识别率下降的问题,提出基于门控注意力网络的调制信号分类识别算法。该算法先对输入信号进行混合数据增强,生成更多维度的样本以便网络更好地提取信号特征;再将处理后的样本信号输入双通道网络(CNN and BiLSTM Parallel),并行提取信号的空间特征和时间特征;最后将提取到的特征输入到门控注意力网络中,自适应地调整特征权重,减少网络复杂度。实验表明,文中提出的算法最高分类准确率为92.3%,优于对比的其他网络模型。
文摘自动调制识别是现代通信系统中一项重要技术。为提高通信系统对不同调制信号间的识别性能,文中首先探索了包含11类调制信号的公开数据集RML2016.10A上原始同相正交(In-phase and Quadrature,IQ)格式数据和经过数据预处理后的幅度和相位(Amplitude and Phase,AP)格式数据的差异;随后,依据原始IQ格式数据和AP格式数据在特征提取过程中对局部相关性及时序特征敏感性的差异,设计了针对空间特征提取的SFE-Block模块、针对长期依赖关系提取的TFE-Block模块,以及联合时空特征提取模块STFE-Block,并将前两者的输出特征作为STFE-Block模块输出特征的重要补充进行特征融合,以全连接(Fully Connected)层负责最终分类。实验结果表明,本模型在数据集RML2016.10A上表现良好。当信噪比(Signal to Noise Ratio,SNR)低于-8 dB时,平均识别精度比其他模型提升7%,而SNR在0~18 dB时,平均识别精度比其他模型提高1%~8%,且在SNR为16 dB时,最高识别精度达92.95%。此外,在RML2016.10B数据集上重复了实验以检验模型泛化性,所得结果同样最优,且当SNR为12 dB时,最高识别精度达到93.6%。
