Modulation Recognition of Satellite Communication Signal Based on Intelligent Analysis of Multi-Fractal Spectrum

YANG Wei-chao; DU Yu; WEN Wei; HOU Shu-wei; XU Chang-zhi; ZHANG Jian-hua

doi:10.12263/DZXB.20210882

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Modulation Recognition of Satellite Communication Signal Based on Intelligent Analysis of Multi-Fractal Spectrum

Intelligent game of electromagnetic spectrum | 更新时间：2025-12-08

- Modulation Recognition of Satellite Communication Signal Based on Intelligent Analysis of Multi-Fractal Spectrum
- ACTA ELECTRONICA SINICA Vol. 50, Issue 6, Pages: 1336-1343(2022)
- 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：
- 基金信息：
- DOI：10.12263/DZXB.20210882
  CLC： TN911.72;
- Received：12 July 2021，
  
  Revised：2021-12-29，
  
  Published：25 June 2022
- 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%.

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Keywords

references

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