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
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
For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the s...For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.展开更多
Modulation recognition has been long investigated in the literature,however,the performance could be severely degraded in multipath fading channels especially for high-order Quadrature Amplitude Modulation(QAM)signals...Modulation recognition has been long investigated in the literature,however,the performance could be severely degraded in multipath fading channels especially for high-order Quadrature Amplitude Modulation(QAM)signals.This could be a critical problem in the broadband maritime wireless communications,where various propagation paths with large differences in the time of arrival are very likely to exist.Specifically,multiple paths may stem from the direct path,the reflection paths from the rough sea surface,and the refraction paths from the atmospheric duct,respectively.To address this issue,we propose a novel blind equalization-aided deep learning(DL)approach to recognize QAM signals in the presence of multipath propagation.The proposed approach consists of two modules:A blind equalization module and a subsequent DL network which employs the structure of ResNet.With predefined searching step-sizes for the blind equalization algorithm,which are designed according to the set of modulation formats of interest,the DL network is trained and tested over various multipath channel parameter settings.It is shown that as compared to the conventional DL approaches without equalization,the proposed method can achieve an improvement in the recognition accuracy up to 30%in severe multipath scenarios,especially in the high SNR regime.Moreover,it efficiently reduces the number of training data that is required.展开更多
Modulation recognition becomes unreliable at low signal-to-noise ratio(SNR)over fading channel.A novel method is proposed to recognize the digital modulated signals with frequency and phase offsets over multi-path fad...Modulation recognition becomes unreliable at low signal-to-noise ratio(SNR)over fading channel.A novel method is proposed to recognize the digital modulated signals with frequency and phase offsets over multi-path fading channels in this paper.This method can overcome the effects of phase offset,Gaussian noise and multi-path fading.To achieve this,firstly,the characteristic parameters search is constructed based on the cyclostationarity of received signals,to overcome the phase offset,Gaussian white noise,and influence caused by multi-path fading.Then,the carrier frequency of the received signal is estimated,and the maximum characteristic parameter is searched around the integer multiple carriers and their vicinities.Finally,the modulation types of the received signal with frequency and phase offsets are classified using decision thresholds.Simulation results demonstrate that the performance of the proposed method is better than the traditional methods when SNR is over 5dB,and that the proposed method is robust to frequency and phase offsets over multipath channels.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
It is particular important to identify the pattern of communication signal quickly and accurately at the airport terminal area with the increasing number of radio equipments.A signal modulation pattern recognition met...It is particular important to identify the pattern of communication signal quickly and accurately at the airport terminal area with the increasing number of radio equipments.A signal modulation pattern recognition method based on compressive sensing and improved residual network is proposed in this work.Firstly,the compressive sensing method is introduced in the signal preprocessing process to discard the redundant components for sampled signals.And the compressed measurement signals are taken as the input of the network.Furthermore,based on a scaled exponential linear units activation function,the residual unit and the residual network are constructed in this work to solve the problem of long training time and indistinguishable sample similar characteristics.Finally,the global residual is introduced into the training network to guarantee the convergence of the network.Simulation results show that the proposed method has higher recognition efficiency and accuracy compared with the state-of-the-art deep learning methods.展开更多
为了提升未来弹性光网络的性能,提出了一种基于幅值密度特征的调制格式识别方法。该方法将幅值密度特征作为改进的Mobile Net V2模型的输入,通过特征识别确定调制格式类型,并引入了归一化注意力机制(NAM),实现对传输信号调制格式的精准...为了提升未来弹性光网络的性能,提出了一种基于幅值密度特征的调制格式识别方法。该方法将幅值密度特征作为改进的Mobile Net V2模型的输入,通过特征识别确定调制格式类型,并引入了归一化注意力机制(NAM),实现对传输信号调制格式的精准识别。在28 GBaud正交相移键控(QPSK)、8电平正交幅度调制(8QAM)、16QAM、32QAM、64QAM和128QAM传输系统中验证了该方案的可行性。实验结果表明:每种调制格式在达到100%识别准确率时所需的最低光信噪比(OSNR)均低于其对应的20%前向纠错(FEC)阈值,而且,在较宽的OSNR范围内达到了99.62%的识别准确率;在存在残余色散的光网络中,该方案仍能保持较高的识别性能。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
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).
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.
基金supported in part by the National Natural Science Foundation of China under Grand No.61871129 and No.61301179Projects of Science and Technology Plan Guangdong Province under Grand No.2014A010101284
文摘For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.
基金the National Natural Science Foundation of China under Grant 61771264,61801114,61501264,61771286the Nantong University-Nantong Joint Research Center for Intelligent Information Technology under Grant No.KFKT2017B01,KFKT2017A04the Natural Science Foundation of Jiangsu Province under Grant BK20170688.
文摘Modulation recognition has been long investigated in the literature,however,the performance could be severely degraded in multipath fading channels especially for high-order Quadrature Amplitude Modulation(QAM)signals.This could be a critical problem in the broadband maritime wireless communications,where various propagation paths with large differences in the time of arrival are very likely to exist.Specifically,multiple paths may stem from the direct path,the reflection paths from the rough sea surface,and the refraction paths from the atmospheric duct,respectively.To address this issue,we propose a novel blind equalization-aided deep learning(DL)approach to recognize QAM signals in the presence of multipath propagation.The proposed approach consists of two modules:A blind equalization module and a subsequent DL network which employs the structure of ResNet.With predefined searching step-sizes for the blind equalization algorithm,which are designed according to the set of modulation formats of interest,the DL network is trained and tested over various multipath channel parameter settings.It is shown that as compared to the conventional DL approaches without equalization,the proposed method can achieve an improvement in the recognition accuracy up to 30%in severe multipath scenarios,especially in the high SNR regime.Moreover,it efficiently reduces the number of training data that is required.
基金supported by the National Natural Science Foundation of China under Grant 62071364 and 62231027in part by the Key Research and Development Program of Shaanxi under Grant 2023-YBGY-249+1 种基金in part by the Key Research and Development Program of Guangxi under Grant 2022AB46002in part by the Fundamental Research Funds for the Central Universities under Grant KYFZ23001.
文摘Modulation recognition becomes unreliable at low signal-to-noise ratio(SNR)over fading channel.A novel method is proposed to recognize the digital modulated signals with frequency and phase offsets over multi-path fading channels in this paper.This method can overcome the effects of phase offset,Gaussian noise and multi-path fading.To achieve this,firstly,the characteristic parameters search is constructed based on the cyclostationarity of received signals,to overcome the phase offset,Gaussian white noise,and influence caused by multi-path fading.Then,the carrier frequency of the received signal is estimated,and the maximum characteristic parameter is searched around the integer multiple carriers and their vicinities.Finally,the modulation types of the received signal with frequency and phase offsets are classified using decision thresholds.Simulation results demonstrate that the performance of the proposed method is better than the traditional methods when SNR is over 5dB,and that the proposed method is robust to frequency and phase offsets over multipath channels.
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.
基金supported by the National Natural Science Foundation of China(No.71874081)Special Financial Grant from China Postdoctoral Science Foundation(No.2017T100366)Open Fund of Hebei Province Key laboratory of Research on data analysis method under dynamic electro-magnetic spectrum situation.
文摘It is particular important to identify the pattern of communication signal quickly and accurately at the airport terminal area with the increasing number of radio equipments.A signal modulation pattern recognition method based on compressive sensing and improved residual network is proposed in this work.Firstly,the compressive sensing method is introduced in the signal preprocessing process to discard the redundant components for sampled signals.And the compressed measurement signals are taken as the input of the network.Furthermore,based on a scaled exponential linear units activation function,the residual unit and the residual network are constructed in this work to solve the problem of long training time and indistinguishable sample similar characteristics.Finally,the global residual is introduced into the training network to guarantee the convergence of the network.Simulation results show that the proposed method has higher recognition efficiency and accuracy compared with the state-of-the-art deep learning methods.
文摘为了提升未来弹性光网络的性能,提出了一种基于幅值密度特征的调制格式识别方法。该方法将幅值密度特征作为改进的Mobile Net V2模型的输入,通过特征识别确定调制格式类型,并引入了归一化注意力机制(NAM),实现对传输信号调制格式的精准识别。在28 GBaud正交相移键控(QPSK)、8电平正交幅度调制(8QAM)、16QAM、32QAM、64QAM和128QAM传输系统中验证了该方案的可行性。实验结果表明:每种调制格式在达到100%识别准确率时所需的最低光信噪比(OSNR)均低于其对应的20%前向纠错(FEC)阈值,而且,在较宽的OSNR范围内达到了99.62%的识别准确率;在存在残余色散的光网络中,该方案仍能保持较高的识别性能。
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。
