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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 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.

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

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维普期刊数据库

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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 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.

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

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维普期刊数据库

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Feature-based automatic modulation recognition Design for Vehicular Network Communication

Feature-based Automatic Modulation Recognition Design for Ve...

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2015中国汽车工程学会年会

作者： Yao Ge Zhan Shu David Daut Department of Electrical and Computer Engineering Rutgers University New Brunswick Department of Civil and Environmental Engineering New Jersey Instiute of Technology

The Software defined radios（SDR）concept[1,2]suggests that future radio systems will support multi-standard,multimode and multiband wireless communications.As described in the survey[3],this technology also could be utilized in the vehicle to vehicle network,where existing multi-standards communication protocol currently.This agile radio system could be utilized through two main processors;automatic modulation recognition（AMR）and Demodulation of digitally modulated communication signals.The AMR method can be described as blind identification of the modulation scheme used to format digital data embedded in a received signal.This technology is studied and developed in this work by utilizing a new feature-based method.

关键词： vehicular network reconfigurable radio automatic modulation recognition digital communication system wavelet

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An Improved Algorithm for Blind Carrier Frequency Estimation with Burst MPSK Transmissions

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Wuhan University Journal of Natural Sciences 2013年第1期18卷 55-58页

作者： YUAN Xiaohua ZHENG Hui ZHAO Zhengyu ZHOU Chen National Key Laboratory of Blind Signals Processing School of Electronic Information Wuhan University

This paper presents an improved non-data-aided algo- rithm for carrier frequency estimation for burst M-ary PSK signals when modulation order M and training symbols are unknown. Unlike data-aided estimation, a phase clustering algorithm is used first to estimate M and modulated information is removed by a vari- able interval linear phase unwrapping. Then, a high-order correlation algorithm with proper correction is present, which reduces the probability of phase ambiguity and promotes anti-noise capability of the estimation. Simulations are given to analyze the unbiased esti- mation range, and the asymptotic performance and symbol number are needed to compare with the former algorithms. The new algo- rithm has a large estimation range close to the theoretical maximum value for non-data-aided estimation and has a better performance than earlier non-data-aided techniques for large frequency offset, low signal-to-noise ratio, and limited symbol numbers.

关键词： burst MPSK signal automatic modulation recognition non-data-aided carrier frequency offset linear phase unwrapping high-order correlation

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