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automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

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Defence Technology（防务技术） 2024年第4期34卷 328-338页

作者： Yangtian Liu Xiaopeng Yan Qiang Liu Tai An Jian Dai Science and Technology on Electromechanical Dynamic Control Laboratory School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China China Research and Development Academy of Machinery Equipment Beijing 100089China

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference ***,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 *** solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is ***,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)***,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 *** 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 *** 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.

关键词： automatic modulation recognition Adaptive denoising Data rearrangement and the 2D FFT(DR2D) Radio fuze

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automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

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Defence Technology（防务技术） 2024年第3期33卷 364-373页

作者： Guanghua Yi Xinhong Hao Xiaopeng Yan Jian Dai Yangtian Liu Yanwen Han Science and Technology on Electromechanical Dynamic Control Laboratory School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China BIT Tangshan Research Institute Beijing 100081China

automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive ***,the AMR of radiation source signals at low SNRs still faces a great ***,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this ***,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing ***,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR ***,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation *** 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 *** recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.

关键词： automatic modulation recognition Radiation source signals Two-dimensional data matrix Residual neural network Depthwise convolution

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automatic modulation recognition Method Based on Channel-Enhanced Convolution and Linear-Angular Attention

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IEEE ACCESS 2025年 13卷 50428-50436页

作者： Gong, An Jia, Anxuan Wu, Shuhui Fan, Bitian Wei, Xintong China Univ Petr East China Qingdao Inst Software Coll Comp Sci & Technol Qingdao 266580 Peoples R China

automatic modulation recognition (AMR) is an electronic signal processing technology designed to automatically identify and classify the modulation type of radio signals. Existing AMR methods suffer from a significant decrease in classification accuracy when the Signal-to-Noise Ratio (SNR) degrades. To enhance the accuracy of AMR methods under low SNR conditions, this paper proposes a novel AMR method based on Channel-Enhanced Convolution and a Linear-Angular Attention Mechanism, named CCLANN (Channel-enhanced Convolutional Linear-Angular Attention Neural Network). This method first utilizes a channel-enhanced deep convolutional module to extract spatial information from the feature maps of the input signal across different channel dimensions. Subsequently, a linear-angular attention mechanism is introduced to effectively extract time-series features from the modulated signals. Experimental results on the public datasets RML2016.10a and RML2016.10b demonstrate that the proposed AMR method improves the average classification accuracy by 2.4% and 2.1%, respectively, compared to existing schemes in the SNR range of -4 dB to 4 dB, while maintaining robustness. Slight improvements in classification accuracy are also observed at SNRs above 0 dB.

关键词： modulation Feature extraction Convolution Accuracy Signal to noise ratio Deep learning Noise Neural networks Data models Convolutional neural networks automatic modulation recognition channel-enhanced convolution linear-angular attention

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automatic modulation recognition of Unknown Interference Signals Based on Graph Model

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第9期13卷 2317-2321页

作者： Zhang, Qiancheng Ji, Hongbing Li, Lin Zhu, Zhigang Xidian Univ Fac Sch Elect Engn Xian 710071 Peoples R China

automatic modulation recognition (AMR), which involves blind identification of interference modulation classes, is an essential technique for maintaining communication security. In this letter, a framework for end-to-end interference sequence recognition is designed by introducing graph signal and graph neural network into the field of interference signals modulation recognition. The framework consists of three modules: channel interaction module, graph mapping and graph feature extraction, and feature fusion classification. The framework focuses, on the one hand, on enhancing the channel information interaction through the proposed channel fusion method and, on the other hand, modelling the sequence as a graph and extracting local features and global features using the graph neural network. Simulation results verify that the method has high performance in the AMR task.

关键词： Interference Feature extraction modulation Noise Graph neural networks Time series analysis Mathematical models automatic modulation recognition graph signal graph neural network channel information interaction

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automatic modulation recognition of Dual-Component Radar Signals Using ResSwinTSwinT Network

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 6405-6418页

作者： Ren, Bing Teh, Kah Chan An, Hongyang Gunawan, Erry Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

automatic modulation recognition plays an important role in military and civilian applications, identifying the modulation format of received signals before signal demodulation. With the increasing complexity and density of the electromagnetic environment, the multicomponent modulation radar signal recognition against various signal-to-noise ratio (SNR) conditions has become a practical and urgent problem. In this article, we propose a dual-component modulation recognition framework, which incorporates the residual Swin transformer denoise network, Swin transformer feature extraction network (SwinT), residual-attention (RA) modulation recognition head, and SNR level classifier and achieves robust recognition performance against various SNR conditions with tolerable complexity and accuracy trade-off. First, the time-frequency analysis is employed to transform dual-component radar signals into time-frequency images (TFIs). Then, the TFIs at various SNR levels are applied to the SwinT, which generates shallow and deep feature representations for the SNR classifier and RA-modulation recognition head, respectively. The ResSwinT is initiated to reconstruct low SNR TFIs only, which are again processed by the SwinT. Finally, the RA-modulation recognition head provides modulation format predictions. The proposed framework can identify randomly combined dual-component radar signals from 12 modulation formats, meanwhile, improving the utilization of the SwinT feature and reducing unnecessary computation of the ResSwinT. Simulation results show that the proposed scheme can obtain an exact match ratio (EMR) of larger than 97% at SNR >-6 dB. At low SNR condition (-12 dB), the ResSwinT can obtain about EMR gain of 20% and the overall framework can achieve EMR of more than 80%, which outperforms other state-of-the-art methods and obtains better generalization capability.

关键词： Signal to noise ratio modulation Transformers Feature extraction Radar Head Time-frequency analysis automatic modulation recognition dual-component radar signals multilabel classification Swin transformer (SwinT)

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automatic modulation recognition of radar signals based on histogram of oriented gradient via improved principal component analysis

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第6期17卷 3053-3061页

作者： Chen, Kuiyu Chen, Si Zhang, Shuning Zhao, Huichang Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing Peoples R China

automatic modulation recognition (AMR) of radar signals plays a critical role in electronic reconnaissance. Current AMR algorithms are mainly based on convolutional neural networks (CNN), which can learn the feature hierarchy by establishing high-level features from low-level features. However, for time-frequency analysis-based methods, distinct low-level features in the time-frequency spectrum can already reflect modulation characteristics. Thus, this study develops a novel approach based on low-level shape descriptors via histograms of oriented gradients (HOG) and support vector machine (SVM). Comparison studies with classic CNN-based methods have also been done to reveal the superiority of the designed approach. Experimental results demonstrate that the HOG-SVM approach has a more efficient performance. To further enhance the classification precision under low signal-to-noise ratios, an improved principal component analysis denoising algorithm is developed to improve signal quality under intense noise background. Experiments based on simulated and measured signals demonstrate that the proposed algorithm can accurately distinguish signals under intense noise environments.

关键词： automatic modulation recognition Radar signals Low-level features Histograms of oriented gradients Support vector machine Improved principal component analysis

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automatic modulation recognition with Deep Learning Algorithms 32

Automatic Modulation Recognition with Deep Learning Algorith...

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32nd IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Camlibel, Aysenur Karakaya, Bahattin Tanc, Yesim Hekim Istanbul Univ Cerrahpasa Muhendisl Fak Elekt Elekt Muhendisligi Bolumu Istanbul Turkiye

ISBN: (纸本)9798350388978;9798350388961

In this study, an automatic modulation classifier based on Convolutional Neural Network (CNN) was developed using deep learning algorithms. A synthetic dataset generated with GNU Radio consisting of eleven modulations at varying signal-to-noise ratios was used for classification. The time-frequency domain images of the complex signals are generated. An efficient approach, Synchrosqueezing Transform (SST), is used to provide time-frequency representations of complex signals. A CNN model has been developed for the classification of time-frequency analysis images produced by the Fourier-Based Synchrosqueezig Transform (FSST) method. The new dataset, consisting of images taken with FSST, has been turned into a three-dimensional input for the designed CNN model. A modulation classifier was prepared by training these inputs with the CNN model created using seven convolution layers and five dense layers. The proposed classifier provided a high performance of eighty-seven percent.

关键词： automatic modulation recognition modulation classification deep learning time frequency analysis CNN SST FSST

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automatic modulation recognition: A Hybrid Approach Using Deep Learning and K-Means Clustering 8th

Automatic Modulation Recognition: A Hybrid Approach Using De...

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2024 World Congress in Computer Science, Computer Engineering and Applied Computing-CSCE

作者： Cavarretta, Connor Hijar-Soria, Mihair Hussain, Nahiyan Jia, Ruting Calif State Univ Northridge Dept Elect & Comp Engn Northridge CA 91330 USA

ISBN: (纸本)9783031856273;9783031856280

This paper introduces a hybrid model for automatic modulation recognition (AMR) that enhances classification accuracy of digital communication signals across various signal-to-noise ratios (SNR). The proposed model integrates a Deep Learning (DL) layer featuring a Convolutional Neural Network (CNN) for initial classification with a subsequent refinement layer using the K-Means Clustering algorithm. The process begins with the continuous wavelet transform (CWT) of the received signal to generate a magnitude scalogram, which is used as an input image to the DL Layer. Due to the inherent loss of phase information in generating the magnitude scalogram, the system activates the K-means layer when the initial classification of the signal is a higher-order Phase Shift Keying (PSK) modulation scheme. This subsequent layer refines the classification by evaluating the In-phase and Quadrature (IQ) data and determines the modulation order of the PSK signal by use of the K-means algorithm. The MATLAB simulations showed that incorporating the K-means layer resulted in a 15% improvement in recognition accuracy, ultimately achieving over 95% accuracy for SNR levels of 15 dB and higher.

关键词： automatic modulation recognition Deep Learning Wavelet Analysis

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TC-MSNet: A Multi-feature Extraction Neural Framework for automatic modulation recognition

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WIRELESS PERSONAL COMMUNICATIONS 2025年第1-2期140卷 377-398页

作者： Wang, Yang Feng, Yongxin Song, Bixue Tian, Binghe Qian, Bo Shenyang Ligong Univ Sch Informat Sci & Engn 6 Nanping Middle Rd Shenyang 110159 Liaoning Peoples R China

In the approaches of automatic modulation recognition (AMR), modulation modes with similar characteristics are prone to be confused by the adverse factors such as noise, inevitably bringing challenges to the accuracy of recognition. Aiming at this kind of problems, this paper proposes TC-MSNet which is a novel multi-scale spatial-temporal features collaboration neural network based on deep learning. TC-MSNet extracts the Temporal Correlation (TC) features and the Multi-Scale Spatial (MSS) features respectively for enhancing the diversity of features extraction, meanwhile, a fusion strategy constructed with selective kernel attention mechanism implement sufficiently collaboration of multi-scale features. Additionally, the Bilinear-Pooling Mechanism (BPM) is adopted to capture the differentiation of fine-grained features, eliminating the confusion among distinct modulation modes to promote AMR accuracy. The simulation experimental results show that, on the RML2016.10a and RML2016.10b, the proposed TC-MSNet not only has better performance compared with existing deep learning-based AMR methods but also possesses stronger ability to eliminate confusion of similar features.

关键词： automatic modulation recognition Deep learning Feature extraction Feature fusion Attention mechanism

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A Novel LSTM Architecture for automatic modulation recognition: Comparative Analysis With Conventional Machine Learning and RNN-Based Approaches

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IEEE ACCESS 2025年 13卷 72526-72543页

作者： Ansari, Sam Mahmoud, Soliman Majzoub, Sohaib Almajali, Eqab Jarndal, Anwar Bonny, Talal Univ Sharjah Res Inst Sci & Engn Sharjah U Arab Emirates Univ Sharjah Elect Engn Dept Sharjah U Arab Emirates Fayoum Univ Elect Engn Dept Al Fayyum 63514 Egypt Univ Sharjah Comp Engn Dept Sharjah U Arab Emirates

The recognition of modulation types in received signals is essential for signal detection and demodulation, with broad applications in telecommunications, defense, and wireless communications. This paper introduces a pioneering approach to automatic modulation recognition (AMR) through the development of a highly optimized long short-term memory (LSTM) network. The proposed framework is engineered to capture intricate temporal dependencies within modulated signals, leveraging a gated architecture that effectively mitigates the vanishing gradient problem. This innovation markedly improves recognition accuracy, particularly in low-SNR conditions where traditional methods are often limited. A defining contribution of this work is the introduction of a novel, adaptive temporal-spectral feature learning mechanism, which seamlessly integrates both temporal and spectral characteristics of the signal. This paradigm eliminates the need for manual feature extraction, enhances interpretability, and significantly boosts classification efficiency. Furthermore, the proposed framework is designed for low-complexity deployment, ensuring its scalability and suitability for next-generation wireless networks and real-time communication systems. The proposed architecture is capable of distinguishing between seven modulation classes: BASK, 4-ASK, BFSK, 4-FSK, BPSK, 4-PSK, and 16-QAM. Performance is evaluated across a broad range of signal-to-noise ratios (SNR), from -10 dB to +30 dB, through extensive simulations. Experimental results demonstrate that the model achieves a recognition accuracy of 99.87% at an SNR of -5 dB, outperforming several conventional machine learning techniques, including multi-layer perceptron (MLP), radial basis function (RBF) networks, adaptive neuro-fuzzy inference systems (ANFIS), decision trees (DT), na & iuml;ve Bayes (NB), support vector machines (SVM), probabilistic neural networks (PNN), k-nearest neighbors (KNN), and ensemble learning models, as well as

关键词： Accuracy modulation Feature extraction Long short term memory Computer architecture Adaptation models Wireless communication Computational modeling Symbols Signal to noise ratio automatic modulation recognition classification conventional algorithms deep learning long short-term memory

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