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
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
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
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
Visual Place Recognition(VPR)technology aims to use visual information to judge the location of agents,which plays an irreplaceable role in tasks such as loop closure detection and relocation.It is well known that pre...Visual Place Recognition(VPR)technology aims to use visual information to judge the location of agents,which plays an irreplaceable role in tasks such as loop closure detection and relocation.It is well known that previous VPR algorithms emphasize the extraction and integration of general image features,while ignoring the mining of salient features that play a key role in the discrimination of VPR tasks.To this end,this paper proposes a Domain-invariant Information Extraction and Optimization Network(DIEONet)for VPR.The core of the algorithm is a newly designed Domain-invariant Information Mining Module(DIMM)and a Multi-sample Joint Triplet Loss(MJT Loss).Specifically,DIMM incorporates the interdependence between different spatial regions of the feature map in the cascaded convolutional unit group,which enhances the model’s attention to the domain-invariant static object class.MJT Loss introduces the“joint processing of multiple samples”mechanism into the original triplet loss,and adds a new distance constraint term for“positive and negative”samples,so that the model can avoid falling into local optimum during training.We demonstrate the effectiveness of our algorithm by conducting extensive experiments on several authoritative benchmarks.In particular,the proposed method achieves the best performance on the TokyoTM dataset with a Recall@1 metric of 92.89%.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
A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters i...A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.展开更多
To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is ...To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction （Polak-Ribiere） 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. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition.展开更多
A hybrid carrier（HC） scheme based on weighted-type fractional Fourier transform（WFRFT） has been proposed recently.While most of the works focus on HC scheme＇s inherent characteristics, little attention is paid to...A hybrid carrier（HC） scheme based on weighted-type fractional Fourier transform（WFRFT） has been proposed recently.While most of the works focus on HC scheme＇s inherent characteristics, little attention is paid to the WFRFT modulation recognition.In this paper, a new theory is provided to recognize the WFRFT modulation based on higher order cumulants（HOC）. First, it is deduced that the optimal WFRFT received order can be obtained through the minimization of 4 th-order cumulants, C_（42）. Then, a combinatorial searching algorithm is designed to minimize C_（42）.Finally, simulation results show that the designed scheme has a high recognition rate and the combinatorial searching algorithm is effective and reliable.展开更多
Based on the features extracted from generalized autoregressive (GAR) model parameters of the received waveform, and the use of multilayer perceptron(MLP) neural network classifier, a new digital modulation recognitio...Based on the features extracted from generalized autoregressive (GAR) model parameters of the received waveform, and the use of multilayer perceptron(MLP) neural network classifier, a new digital modulation recognition method is proposed in this paper. Because of the better noise suppression ability of the GAR model and the powerful pattern classification capacity of the MLP neural network classifier, the new method can significantly improve the recognition performance in lower SNR with better robustness. To assess the performance of the new method, computer simulations are also performed.展开更多
This paper presents a joint high order statistics （HOS） and signal-to-noise ratio （SNR） algorithm for the recognition of multiple-input multiple-output （MIMO） radar signal without a priori knowledge of the signa...This paper presents a joint high order statistics （HOS） and signal-to-noise ratio （SNR） algorithm for the recognition of multiple-input multiple-output （MIMO） radar signal without a priori knowledge of the signal parameters. This method is capable of recognizing the MIMO radar signal as well as discriminating it from single-carrier signal adopted by conventional radar. Meanwhile, the sub-carrier number of the none-coding MIMO radar signal is estimated. Extensive simulations are carried out in different operating conditions. Simulation results prove the feasibility and indicate that the recognition probability could reach over 90% when the value of SNR is above 0 dB.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
This paper mainly studies the data characteristics of high order cumulants using digitally modulated signals, and constructs the identification feature parameters that can distinguish the signal modulation type by the...This paper mainly studies the data characteristics of high order cumulants using digitally modulated signals, and constructs the identification feature parameters that can distinguish the signal modulation type by the high-order cumulants data of the digital modulation signal. Set the identification signal modulation type determination threshold based on the value of the identification feature parameter. The identification feature parameter value of the signal modulation type is compared with the set determination threshold, to realize the recognition of digital modulation signal. This identification method is implemented based on MATLAB design, with a 2ASK (2-ary Amplitude Shift Keying) signal, 4ASK (4-ary Amplitude Shift Keying) signal, 2PSK (2-ary Phase Shift Keying) signal, 4PSK (4-ary Phase Shift Keying) signal, 2FSK (2-ary Frequency Shift Keying) signal, 4FSK (4-ary Frequency Shift Keying) signal. The second, fourth and sixth order cumulants of the six signals were analyzed. Calculate the selected identification feature parameter value and the determination threshold to identify the six signals. The six signals have made MATLAB identification simulation. Simulation results show that this method is feasible and has high recognition rate. Simulation results verify that such recognition methods maintain a high recognition rate under conditions with low signal-to-noise ratio. This identification method can be extended to more MASK (M-ary Amplitude Shift Keying), MPSK (M-ary Phase Shift Keying), MFSK (M-ary Frequency Shift Keying), MQAM (M-ary Quadrature Amplitude Modulation) signal identification.展开更多
To classify the frequency modulation signal, this paper employs a parameter invariant filter, which can transfer the frequency modulated information to variety of its envelope, and then extracts the histogram feature ...To classify the frequency modulation signal, this paper employs a parameter invariant filter, which can transfer the frequency modulated information to variety of its envelope, and then extracts the histogram feature to classify the modulation type. This method can efficiently classify the type of a signal such as frequency modulation （FM）, binary frequency shift keyiing （BFSK）, quadrature frequency shift keying （QFSK）, 8-ary frequency shift keying （8FSK）, etc. It can easily be realized and is especially suitable to applications in software radio.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
A novel approach （HGO-EAC） for hybrid genetic op-timization （GO） with elite ant colony （EAC） is proposed for the automatic modulation recognition of communication signals,through which we improve the basic ant c...A novel approach （HGO-EAC） for hybrid genetic op-timization （GO） with elite ant colony （EAC） is proposed for the automatic modulation recognition of communication signals,through which we improve the basic ant colony algorithms by referencing elite strategy and present a new fusion strategy for genetic optimization and elite ant colony. This approach is used to train the neural networks as the classifier for modulation. Simula-tion results indicate good performance on an additive white Gaus-sian noise （AWGN） channel,with recognition rate reaching to 70% especially for CW even at signal-to-noise ratios as low as 5 dB. This approach can achieve a high recognition rate for the typical modulations such as CW,4ASK,4FSK,BPSK,and QAM16. Test result shows that it has better performance than BP algorithm and basic ant colony algorithms by achieving faster training and stronger robustness.展开更多
In recent years,the need for a fast,efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperativ...In recent years,the need for a fast,efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperative communication scenarios,the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them.The recent advancements in both Machine Learning(ML)and Deep Learning(DL)models demand the development of effective modulation recognition models with self-learning capability.In this background,the current research article designs aDeep Learning enabled Intelligent Modulation Recognition of Communication Signal(DLIMR-CS)technique for next-generation networks.The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals.In addition,the fractal feature extraction process is appliedwith the help of the Sevcik Fractal Dimension(SFD)approach.Then,the extracted features are fed into the Deep Variational Autoencoder(DVAE)model for the classification of the modulated signals.In order to improve the classification performance of the DVAE model,the Tunicate Swarm Algorithm(TSA)is used to finetune the hyperparameters involved in DVAE model.A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model.The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.展开更多
In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,etc.It becomes essential to design a safe and robus...In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,etc.It becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex platform.Several works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so on.In addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal classification.In this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals.The proposed CSM-FFDNN model involves two major processes namely FE and classification.The proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated signals.Besides,the extracted features are fed into the DNN model for modulation signal classification.To improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model.The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.展开更多
A neural network integrated classifier(NNIC) designed with a new modulation recognition algorithm based on the decision-making tree is proposed in this paper.Firstly,instantaneous parameters are extracted in the time ...A neural network integrated classifier(NNIC) designed with a new modulation recognition algorithm based on the decision-making tree is proposed in this paper.Firstly,instantaneous parameters are extracted in the time domain by the coordinated rotation digital computer(CORDIC) algorithm based on the extended convergence domain and feature parameters of frequency spectrum and power spectrum are extracted by the time-frequency analysis method.All pattern identification parameters are calculated under the I/Q orthogonal two-channel structure,and constructed into the feature vector set.Next,the classifier is designed according to the modulation pattern and recognition performance of the feature parameter set,the optimum threshold is selected for each feature parameter based on the decision-making mechanism in a single classifier,multi-source information fusion and modulation recognition are realized based on feature parameter judge process in the NNIC.Simulation results show NNIC is competent for all modulation recognitions,8 kinds of digital modulated signals are effectively identified,which shows the recognition rate and anti-interference capability at low SNR are improved greatly,the overall recognition rate can reach 100%when SNR is12dB.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.
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 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.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region under grant number 2022D01B186.
文摘Visual Place Recognition(VPR)technology aims to use visual information to judge the location of agents,which plays an irreplaceable role in tasks such as loop closure detection and relocation.It is well known that previous VPR algorithms emphasize the extraction and integration of general image features,while ignoring the mining of salient features that play a key role in the discrimination of VPR tasks.To this end,this paper proposes a Domain-invariant Information Extraction and Optimization Network(DIEONet)for VPR.The core of the algorithm is a newly designed Domain-invariant Information Mining Module(DIMM)and a Multi-sample Joint Triplet Loss(MJT Loss).Specifically,DIMM incorporates the interdependence between different spatial regions of the feature map in the cascaded convolutional unit group,which enhances the model’s attention to the domain-invariant static object class.MJT Loss introduces the“joint processing of multiple samples”mechanism into the original triplet loss,and adds a new distance constraint term for“positive and negative”samples,so that the model can avoid falling into local optimum during training.We demonstrate the effectiveness of our algorithm by conducting extensive experiments on several authoritative benchmarks.In particular,the proposed method achieves the best performance on the TokyoTM dataset with a Recall@1 metric of 92.89%.
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(61401196)the Jiangsu Provincial Natural Science Foundation of China(BK20140954)+1 种基金the Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory(KX152600015/ITD-U15006)the Beijing Shengfeifan Electronic System Technology Development Co.,Ltd(KY10800150036)
文摘A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.
基金supported by the National Natural Science Foundation of China(6107207061301179)the National Science and Technology Major Project(2010ZX03006-002-04)
文摘To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction （Polak-Ribiere） 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. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition.
基金supported by the National Natural Science Foundation of China(6127125061571460)
文摘A hybrid carrier（HC） scheme based on weighted-type fractional Fourier transform（WFRFT） has been proposed recently.While most of the works focus on HC scheme＇s inherent characteristics, little attention is paid to the WFRFT modulation recognition.In this paper, a new theory is provided to recognize the WFRFT modulation based on higher order cumulants（HOC）. First, it is deduced that the optimal WFRFT received order can be obtained through the minimization of 4 th-order cumulants, C_（42）. Then, a combinatorial searching algorithm is designed to minimize C_（42）.Finally, simulation results show that the designed scheme has a high recognition rate and the combinatorial searching algorithm is effective and reliable.
文摘Based on the features extracted from generalized autoregressive (GAR) model parameters of the received waveform, and the use of multilayer perceptron(MLP) neural network classifier, a new digital modulation recognition method is proposed in this paper. Because of the better noise suppression ability of the GAR model and the powerful pattern classification capacity of the MLP neural network classifier, the new method can significantly improve the recognition performance in lower SNR with better robustness. To assess the performance of the new method, computer simulations are also performed.
基金supported by the Foundation of Chinese People’s Liberation Army General Equipment Department(41101020303)
文摘This paper presents a joint high order statistics （HOS） and signal-to-noise ratio （SNR） algorithm for the recognition of multiple-input multiple-output （MIMO） radar signal without a priori knowledge of the signal parameters. This method is capable of recognizing the MIMO radar signal as well as discriminating it from single-carrier signal adopted by conventional radar. Meanwhile, the sub-carrier number of the none-coding MIMO radar signal is estimated. Extensive simulations are carried out in different operating conditions. Simulation results prove the feasibility and indicate that the recognition probability could reach over 90% when the value of SNR is above 0 dB.
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This paper mainly studies the data characteristics of high order cumulants using digitally modulated signals, and constructs the identification feature parameters that can distinguish the signal modulation type by the high-order cumulants data of the digital modulation signal. Set the identification signal modulation type determination threshold based on the value of the identification feature parameter. The identification feature parameter value of the signal modulation type is compared with the set determination threshold, to realize the recognition of digital modulation signal. This identification method is implemented based on MATLAB design, with a 2ASK (2-ary Amplitude Shift Keying) signal, 4ASK (4-ary Amplitude Shift Keying) signal, 2PSK (2-ary Phase Shift Keying) signal, 4PSK (4-ary Phase Shift Keying) signal, 2FSK (2-ary Frequency Shift Keying) signal, 4FSK (4-ary Frequency Shift Keying) signal. The second, fourth and sixth order cumulants of the six signals were analyzed. Calculate the selected identification feature parameter value and the determination threshold to identify the six signals. The six signals have made MATLAB identification simulation. Simulation results show that this method is feasible and has high recognition rate. Simulation results verify that such recognition methods maintain a high recognition rate under conditions with low signal-to-noise ratio. This identification method can be extended to more MASK (M-ary Amplitude Shift Keying), MPSK (M-ary Phase Shift Keying), MFSK (M-ary Frequency Shift Keying), MQAM (M-ary Quadrature Amplitude Modulation) signal identification.
基金Project supported by National High-Technology Research and De-velopment Program(Grant No .863 -2002AA119010)
文摘To classify the frequency modulation signal, this paper employs a parameter invariant filter, which can transfer the frequency modulated information to variety of its envelope, and then extracts the histogram feature to classify the modulation type. This method can efficiently classify the type of a signal such as frequency modulation （FM）, binary frequency shift keyiing （BFSK）, quadrature frequency shift keying （QFSK）, 8-ary frequency shift keying （8FSK）, etc. It can easily be realized and is especially suitable to applications in software radio.
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 (41001195)
文摘A novel approach （HGO-EAC） for hybrid genetic op-timization （GO） with elite ant colony （EAC） is proposed for the automatic modulation recognition of communication signals,through which we improve the basic ant colony algorithms by referencing elite strategy and present a new fusion strategy for genetic optimization and elite ant colony. This approach is used to train the neural networks as the classifier for modulation. Simula-tion results indicate good performance on an additive white Gaus-sian noise （AWGN） channel,with recognition rate reaching to 70% especially for CW even at signal-to-noise ratios as low as 5 dB. This approach can achieve a high recognition rate for the typical modulations such as CW,4ASK,4FSK,BPSK,and QAM16. Test result shows that it has better performance than BP algorithm and basic ant colony algorithms by achieving faster training and stronger robustness.
文摘In recent years,the need for a fast,efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperative communication scenarios,the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them.The recent advancements in both Machine Learning(ML)and Deep Learning(DL)models demand the development of effective modulation recognition models with self-learning capability.In this background,the current research article designs aDeep Learning enabled Intelligent Modulation Recognition of Communication Signal(DLIMR-CS)technique for next-generation networks.The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals.In addition,the fractal feature extraction process is appliedwith the help of the Sevcik Fractal Dimension(SFD)approach.Then,the extracted features are fed into the Deep Variational Autoencoder(DVAE)model for the classification of the modulated signals.In order to improve the classification performance of the DVAE model,the Tunicate Swarm Algorithm(TSA)is used to finetune the hyperparameters involved in DVAE model.A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model.The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1F1A1063319).
文摘In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,etc.It becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex platform.Several works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so on.In addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal classification.In this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals.The proposed CSM-FFDNN model involves two major processes namely FE and classification.The proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated signals.Besides,the extracted features are fed into the DNN model for modulation signal classification.To improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model.The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.
基金Supported by the National Natural Science Foundation of China(No.61001049)Key Laboratory of Computer Architecture Opening Topic Fund Subsidization(CARCH201103)Beijing Natural Science Foundation(No.Z2002012201101)
文摘A neural network integrated classifier(NNIC) designed with a new modulation recognition algorithm based on the decision-making tree is proposed in this paper.Firstly,instantaneous parameters are extracted in the time domain by the coordinated rotation digital computer(CORDIC) algorithm based on the extended convergence domain and feature parameters of frequency spectrum and power spectrum are extracted by the time-frequency analysis method.All pattern identification parameters are calculated under the I/Q orthogonal two-channel structure,and constructed into the feature vector set.Next,the classifier is designed according to the modulation pattern and recognition performance of the feature parameter set,the optimum threshold is selected for each feature parameter based on the decision-making mechanism in a single classifier,multi-source information fusion and modulation recognition are realized based on feature parameter judge process in the NNIC.Simulation results show NNIC is competent for all modulation recognitions,8 kinds of digital modulated signals are effectively identified,which shows the recognition rate and anti-interference capability at low SNR are improved greatly,the overall recognition rate can reach 100%when SNR is12dB.
