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
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
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
This paper presents work on modulated signal recognition using an artificial neural network （ANN） developed using the Python programme language. The study is basically on the analysis of analog modulated signals. Fo...This paper presents work on modulated signal recognition using an artificial neural network （ANN） developed using the Python programme language. The study is basically on the analysis of analog modulated signals. Four of the best-known analog modulation types are considered namely： amplitude modulation （AM）, double sideband （DSB） modulation, single sideband （SSB） modulation and frequency modulation （FM）. Computer simulations of the four modulated signals are carried out using MATLAB. MATLAB code is used in simulating the analog signals as well as the power spectral density of each of the analog modulated signals. In achieving an accurate classification of each of the modulated signals, extensive simulations are performed for the training of the artificial neural network. The results of the study show accurate and correct performance of the developed automatic modulation recognition with average success rate above 99.5%.展开更多
In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to m...In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.展开更多
With the deepening development of communication technology,the technology of automatic modulation and recognition of communication signals has been more and more widely used in military and civilian fields.This paper ...With the deepening development of communication technology,the technology of automatic modulation and recognition of communication signals has been more and more widely used in military and civilian fields.This paper mainly studies the implementation of automatic modulation recognition using Deep Learning as a computing tool,focusing on CNN neural network and LSTM neural network,and conducting simulation experiments on public data sets.Based on the original CNN neural network,this paper introduces the structure of LSTM neural network and combines the advantages of the two types of neural networks to explore a combined neural network that is superior to the originally used CNN network.The experimental results of this thesis show that introducing the features of dynamic time series modeling of LSTM networks into Deep Learning networks can capture the global and local information of signals more effectively and improve the accuracy of neural networks in automatic modulation recognition.展开更多
Aiming at the problem that the recognition rate of existing Automatic Modulation Recognition(AMR)models needs to be improved under high signal-to-noise ratio conditions,a model consisting of phase transformation,resid...Aiming at the problem that the recognition rate of existing Automatic Modulation Recognition(AMR)models needs to be improved under high signal-to-noise ratio conditions,a model consisting of phase transformation,residual Convolutional Neural Network(CNN)network and bidirectional Long Short-Term Memory(LSTM)network is proposed.First,the DeepSig RadioML 2018.01A dataset is normalized as the model input;the phase parameter is extracted through the phase recognition module,and then the phase is corrected according to the phase parameter;then the spatial features are extracted through the residual CNN network to avoid gradient vanishing and explosion;then the data is passed to the bidirectional LSTM network to extract the bidirectional time series features of the data;finally,the deep neural network is used for classification and recognition.Experimental results show that under high signal-to-noise ratio conditions,the model improves the recognition rate of modulation modes such as 16PSK,and the highest recognition rate and average recognition rate reach 96.79%and 62.13%respectively.Compared with other existing models,the overall optimization of recognition rate and model efficiency is achieved.展开更多
为提升调制识别的准确性和鲁棒性,本文提出了一种全新的双模态混合调制识别模型.模型同时考虑原始时域同相正交(in-phase and quadrature,I/Q)和幅度相位(amplitude and phase,A/P)双模态数据以探索信号的时空相关性.采用双路对称结构对...为提升调制识别的准确性和鲁棒性,本文提出了一种全新的双模态混合调制识别模型.模型同时考虑原始时域同相正交(in-phase and quadrature,I/Q)和幅度相位(amplitude and phase,A/P)双模态数据以探索信号的时空相关性.采用双路对称结构对A/P模态数据进一步处理,更有效地学习数据间的重复特征,避免信息冗余.模型中引入双向长短时记忆网络(bidirectional long short-term memory network,BiLSTM),利用其双向时序特征提取能力,增强模型对复杂时序信息的理解.实验结果表明,所提模型在数据集RadioML2016.10A上表现良好.当SNR低于−8 dB时,平均识别精度比主流模型提升6%,而SNR在0–18 dB时,平均识别精度比主流模型提高2%–10%,且在SNR为16 dB时,识别精度高达94.32%.另外,将模型迁移到数据集RadioML2016.10B所得结果同样最优,且当SNR为18 dB时识别精度高达93.91%.展开更多
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.
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This paper presents work on modulated signal recognition using an artificial neural network （ANN） developed using the Python programme language. The study is basically on the analysis of analog modulated signals. Four of the best-known analog modulation types are considered namely： amplitude modulation （AM）, double sideband （DSB） modulation, single sideband （SSB） modulation and frequency modulation （FM）. Computer simulations of the four modulated signals are carried out using MATLAB. MATLAB code is used in simulating the analog signals as well as the power spectral density of each of the analog modulated signals. In achieving an accurate classification of each of the modulated signals, extensive simulations are performed for the training of the artificial neural network. The results of the study show accurate and correct performance of the developed automatic modulation recognition with average success rate above 99.5%.
文摘In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.
文摘With the deepening development of communication technology,the technology of automatic modulation and recognition of communication signals has been more and more widely used in military and civilian fields.This paper mainly studies the implementation of automatic modulation recognition using Deep Learning as a computing tool,focusing on CNN neural network and LSTM neural network,and conducting simulation experiments on public data sets.Based on the original CNN neural network,this paper introduces the structure of LSTM neural network and combines the advantages of the two types of neural networks to explore a combined neural network that is superior to the originally used CNN network.The experimental results of this thesis show that introducing the features of dynamic time series modeling of LSTM networks into Deep Learning networks can capture the global and local information of signals more effectively and improve the accuracy of neural networks in automatic modulation recognition.
文摘Aiming at the problem that the recognition rate of existing Automatic Modulation Recognition(AMR)models needs to be improved under high signal-to-noise ratio conditions,a model consisting of phase transformation,residual Convolutional Neural Network(CNN)network and bidirectional Long Short-Term Memory(LSTM)network is proposed.First,the DeepSig RadioML 2018.01A dataset is normalized as the model input;the phase parameter is extracted through the phase recognition module,and then the phase is corrected according to the phase parameter;then the spatial features are extracted through the residual CNN network to avoid gradient vanishing and explosion;then the data is passed to the bidirectional LSTM network to extract the bidirectional time series features of the data;finally,the deep neural network is used for classification and recognition.Experimental results show that under high signal-to-noise ratio conditions,the model improves the recognition rate of modulation modes such as 16PSK,and the highest recognition rate and average recognition rate reach 96.79%and 62.13%respectively.Compared with other existing models,the overall optimization of recognition rate and model efficiency is achieved.
文摘为提升调制识别的准确性和鲁棒性,本文提出了一种全新的双模态混合调制识别模型.模型同时考虑原始时域同相正交(in-phase and quadrature,I/Q)和幅度相位(amplitude and phase,A/P)双模态数据以探索信号的时空相关性.采用双路对称结构对A/P模态数据进一步处理,更有效地学习数据间的重复特征,避免信息冗余.模型中引入双向长短时记忆网络(bidirectional long short-term memory network,BiLSTM),利用其双向时序特征提取能力,增强模型对复杂时序信息的理解.实验结果表明,所提模型在数据集RadioML2016.10A上表现良好.当SNR低于−8 dB时,平均识别精度比主流模型提升6%,而SNR在0–18 dB时,平均识别精度比主流模型提高2%–10%,且在SNR为16 dB时,识别精度高达94.32%.另外,将模型迁移到数据集RadioML2016.10B所得结果同样最优,且当SNR为18 dB时识别精度高达93.91%.
