基于多重分形谱智能分析的卫星信号调制识别研究

杨伟超; 杜宇; 文伟; 侯舒维; 徐常志; 张建华

doi:10.12263/DZXB.20210882

您当前的位置：

首页 >

文章列表页 >

基于多重分形谱智能分析的卫星信号调制识别研究

电磁频谱智能+ | 更新时间：2025-12-08

- 基于多重分形谱智能分析的卫星信号调制识别研究
- Modulation Recognition of Satellite Communication Signal Based on Intelligent Analysis of Multi-Fractal Spectrum
- 电子学报 2022年50卷第6期页码：1336-1343
- 作者机构：
  
  中国空间技术研究院西安分院，陕西西安 710100
- 作者简介：
  
  [ "杨伟超男，1983年出生，河南林州人.现为中国空间技术研究院西安分院高级工程师.主要研究方向为航天电子侦察、智能电磁频谱战等.E-mail: yangwc@cast504.com" ]
  [ "杜宇女，1987年出生，黑龙江哈尔滨人.现为中国空间技术研究院西安分院工程师.主要研究方向为高速数传、通信信号处理等.E-mail: duy@cast504.com" ]
- 基金信息：
  
  军委科技委基础加强计划（军173）重点基础研究项目(2020-JCJQ-ZD-071)
- DOI：10.12263/DZXB.20210882
  中图分类号： TN911.72;
- 收稿：2021-07-12，
  
  修回：2021-12-29，
  
  纸质出版：2022-06-25
- 稿件说明：
移动端阅览
杨伟超,杜宇,文伟等.基于多重分形谱智能分析的卫星信号调制识别研究[J].电子学报,2022,50(06):1336-1343.

YANG Wei-chao,DU Yu,WEN Wei,et al.Modulation Recognition of Satellite Communication Signal Based on Intelligent Analysis of Multi-Fractal Spectrum[J].ACTA ELECTRONICA SINICA,2022,50(06):1336-1343.
杨伟超,杜宇,文伟等.基于多重分形谱智能分析的卫星信号调制识别研究[J].电子学报,2022,50(06):1336-1343. DOI： 10.12263/DZXB.20210882.

YANG Wei-chao,DU Yu,WEN Wei,et al.Modulation Recognition of Satellite Communication Signal Based on Intelligent Analysis of Multi-Fractal Spectrum[J].ACTA ELECTRONICA SINICA,2022,50(06):1336-1343. DOI： 10.12263/DZXB.20210882.

摘要

调制方式识别是电磁频谱战中的关键技术之一，已有星上识别方法智能化程度低、适应性差.针对此类问题，提出了一种基于多重分形谱和深度学习相结合的智能识别方法.首先分析了常见卫星通信信号的多重分形特性，构建了多重分形特征域矩阵.在此基础上，将该特征矩阵与深度学习残差网络相结合，并根据多尺度思想对残差网络结构进行了优化改进，改进后残差网络的多层自主细节特征提取优势完美契合了多重分形谱多尺度特征刻画能力，最终实现了卫星通信信号调制方式的有效识别.仿真结果表明，该方法具有较好的识别性能，当信噪比不低于1 dB时，平均识别率大于89%.

Abstract

Modulation recognition is one of the key technologies in satellite communication anti-interference and interference analysis. The existing on-board recognition methods have low intelligence degree and poor adaptability. In order to solve these problems

an intelligent recognition method based on multi-fractal spectrum and deep learning is proposed. Firstly

the multi-fractal spectrum characteristics of common satellite communication signals are analyzed

a multi-fractal eigendomain matrix is constructed. On this basis

the eigendomain matrix is combined with the deep learning residual network

and the structure of the residual network is optimized and improved according to the multi-scale idea

and the multi-layer autonomous detail feature extraction advantage of the improved residual network perfectly corresponds to the multi-scale feature characterization capability of the multi-fractal spectrum. Finally

the modulation of satellite communication signal is effectively recognized. The simulation results show that this method has good recognition performance

when the SNR is not lower than 1 dB

the average recognition rate is greater than 89%.

关键词

Keywords

references

ABDEL-MONEIM M A , EL-SHAFAI W , ABDEL-SALAM N , et al . A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends [J]. International Journal of Communication Systems , 2021 , 34 ( 10 ): e4762 - e4798 .

李正权 , 林媛 , 李梦雅 , 等 . 基于判别式受限玻尔兹曼机的数字调制识别 [J]. 通信学报 , 2021 , 42 ( 2 ): 81 - 91 .

LI Z Q , LIN Y , LI M Y , et al . Digital modulation recognition based on discriminative restricted Boltzmann machine [J]. Journal on Communications , 2021 , 42 ( 2 ): 81 - 91 . (in Chinese)

SIMIC M , STANKOVIC M , ORLIC V D . Automatic modulation classification of real signals in AWGN channel based on sixth-order cumulants [J]. Radioengineering , 2021 , 30 ( 1 ): 204 - 214 .

SHI Q H , KARASAWA Y . Automatic modulation identification based on the probability density function of signal phase [J]. IEEE Transactions on Communications , 2012 , 60 ( 4 ): 1033 - 1044 .

孙姝君 , 彭盛亮 , 姚育东 , 等 . 基于深度学习的调制识别综述 [J]. 电信科学 , 2021 , 37 ( 5 ): 82 - 90 .

SUN S J , PENG S L , YAO Y D , et al . A survey of deep learning based modulation recognition [J]. Telecommunications Science , 2021 , 37 ( 5 ): 82 - 90 . (in Chinese)

PENG S L , JIANG H Y , WANG H X , et al . Modulation classification based on signal constellation diagrams and deep learning [J]. IEEE Transactions on Neural Networks and Learning Systems , 2019 , 30 ( 3 ): 718 - 727 .

陈昌美 , 李艳斌 . 基于卷积神经网络的调制样式识别研究 [J]. 信息技术 , 2020 , 44 ( 1 ): 101 - 106 .

CHEN C M , LI Y B . Research on modulation pattern recognition based on convolutional neural network [J]. Information Technology , 2020 , 44 ( 1 ): 101 - 106 . (in Chinese)

许华 , 苟泽中 , 蒋磊 , 等 . 适用于样本分布差异的迁移学习调制识别算法 [J]. 华中科技大学学报(自然科学版) , 2021 , 49 ( 4 ): 127 - 132 .

XU H , GOU Z Z , JIANG L , et al . Transfer learning modulation recognition algorithm for differences in sample distribution [J]. Journal of Huazhong University of Science and Technology (Natural Science Edition) , 2021 , 49 ( 4 ): 127 - 132 . (in Chinese)

LIU K , GAO W J , HUANG Q H . Automatic modulation recognition based on a DCN-BiLSTM network [J]. Sensors , 2021 , 21 ( 5 ): 1577 - 1584 .

AL-NUAIMI D H , AKBAR M F , SALMAN L B , et al . AMC2N: automatic modulation classification using feature clustering-based two-lane capsule networks [J]. Electronics , 2021 , 10 ( 1 ): 76 - 108 .

李东瑾 , 杨瑞娟 , 李晓柏 , 等 . 基于栈式稀疏降噪自编码网络的辐射源调制识别 [J]. 电子学报 , 2020 , 48 ( 6 ): 1198 - 1204 .

LI D J , YANG R J , LI X B , et al . Emitter signal modulation recognition based on stacked sparse denoising auto-encoders [J]. Acta Electronica Sinica , 2020 , 48 ( 6 ): 1198 - 1204 . (in Chinese)

陈红 , 蔡晓霞 , 徐云 , 等 . 基于多重分形特征的通信调制方式识别研究 [J]. 电子与信息学报 , 2016 , 38 ( 4 ): 863 - 869 .

CHEN H , CAI X X , XU Y , et al . Communication modulation recognition based on multi-fractal dimension characteristics [J]. Journal of Electronics & Information Technology , 2016 , 38 ( 4 ): 863 - 869 . (in Chinese)

ZHAO C H , YANG W C . Modulation recognition of MFSK signals based on multifractal spectrum [J]. Wireless Personal Communications , 2013 , 72 ( 4 ): 1903 - 1914 .

杨伟超 . Alpha稳定分布噪声下通信信号调制识别研究 [D]. 哈尔滨 : 哈尔滨工程大学 , 2012 .

YANG W C . Modulation Recognition for Communication Signals in the Alpha-Stable Distribution Noise [D]. Harbin : Harbin Engineering University , 2012 . (in Chinese)

ZHANG D M , WANG C , LI C Y , et al . Multi-fractal detrended fluctuation half-spectrum analysis of HRV [J]. The Journal of Engineering , 2019 , 2019( 22 ): 8315 - 8318 .

王书豪 , 阮怀林 . 基于切片双谱多重分形特征的雷达信号识别算法 [J]. 探测与控制学报 , 2019 , 41 ( 5 ): 66 - 70 .

WANG S H , RUAN H L . A radar signal recognition method based on multi-fractal dimension characteristics of bispectrum diagonal slice [J]. Journal of Detection & Control , 2019 , 41 ( 5 ): 66 - 70 . (in Chinese)

钟明寿 , 龙源 , 谢全民 , 等 . 基于分形盒维数和多重分形的爆破地震波信号分析 [J]. 振动与冲击 , 2010 , 29 ( 1 ): 7 - 11, 233 .

ZHONG M S , LONG Y , XIE Q M , et al . Signal analysis for blasting seismic wave based on fractal box-dimension and multi-fractal [J]. Journal of Vibration and Shock , 2010 , 29 ( 1 ): 7 - 11, 233 . (in Chinese)

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas : IEEE , 2016 : 770 - 778 .

HE F X , LIU T L , TAO D C . Why ResNet works? Rresiduals generalize [J]. IEEE Transactions on Neural Networks and Learning Systems , 2020 , 31 ( 12 ): 5349 - 5362 .

BI N , CHEN J H , TAN J . The handwritten Chinese character recognition uses convolutional neural networks with the GoogLeNet [J]. International Journal of Pattern Recognition and Artificial Intelligence , 2019 , 33 ( 11 ): 1940016 .

LIU Z H , WU J Z , FU L S , et al . Improved kiwifruit detection using pre-trained VGG16 with RGB and NIR information fusion [J]. IEEE Access , 2019 , 8 : 2327 - 2336 .

ZHANG W , LI J J , QIU X Y . SAR image super-resolution using deep residual SqueezeNet [C]// AIIPCC'19: Proceedings of the International Conference on Artificial Intelligence , Information Processing and Cloud Computing . Sanya : ACM , 2019 : 1 - 5 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

低信噪比下基于分裂EfficientNet网络的雷达信号调制方式识别

面向时序异常检测的可变视距多向扫描方法

基于稀疏平滑自蒸馏的差分隐私深度学习方法