基于图像深度学习的无线电信号识别

周鑫; 何晓新; 郑昌文

doi:10.11959/j.issn.1000-436x.2019167

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基于图像深度学习的无线电信号识别

学术论文 | 更新时间：2024-06-05

- 基于图像深度学习的无线电信号识别
- Radio signal recognition based on image deep learning
- 通信学报 2019年40卷第7期页码：114-125
- 作者机构：
  
  中国科学院软件研究所天基综合信息系统重点实验室，北京 100190
- 作者简介：
  
  [ "周鑫（1986- ），男，河南项城人，博士，中国科学院软件研究所副研究员、硕士生导师，主要研究方向为认知无线网络、无线信道接入、射频机器学习等。" ]
  [ "何晓新（1966- ），女，湖南衡阳人，中国科学院软件研究所研究员，主要研究方向为认知无线电、通信指挥系统等。" ]
  [ "郑昌文（1969- ），男，湖北大冶人，博士，中国科学院软件研究所研究员、博士生导师，主要研究方向为计算机图形学、空间系统仿真、智能搜索等。" ]
- 基金信息：
  
  国防科技创新特区基金资助项目
- DOI：10.11959/j.issn.1000-436x.2019167
  中图分类号： TN971
- 网络出版日期：2019-07，
  
  纸质出版日期：2019-07-25
- 稿件说明：
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周鑫, 何晓新, 郑昌文. 基于图像深度学习的无线电信号识别[J]. 通信学报, 2019,40(7):114-125.

Xin ZHOU, Xiaoxin HE, Changwen ZHENG. Radio signal recognition based on image deep learning[J]. Journal on communications, 2019, 40(7): 114-125.
周鑫, 何晓新, 郑昌文. 基于图像深度学习的无线电信号识别[J]. 通信学报, 2019,40(7):114-125. DOI： 10.11959/j.issn.1000-436x.2019167.

Xin ZHOU, Xiaoxin HE, Changwen ZHENG. Radio signal recognition based on image deep learning[J]. Journal on communications, 2019, 40(7): 114-125. DOI： 10.11959/j.issn.1000-436x.2019167.

摘要

提出了一种利用图像深度学习解决无线电信号识别问题的技术思路。首先把无线电信号具象化为一张二维图片，将无线电信号识别问题转化为图像识别领域的目标检测问题；进而充分利用人工智能在图像识别领域的先进成果，提高无线电信号识别的智能化水平和复杂电磁环境下的识别能力。基于该思路，提出了一种基于图像深度学习的无线电信号识别算法——RadioImageDet 算法。实验结果表明，所提算法能有效识别无线电信号的波形类型和时/频坐标，在实地采集的12种、4 740个样本的数据集中，识别准确率达到86.04%，mAP值达到77.72，检测时间在中等配置的台式计算机上仅需33 ms，充分验证了所提思路的可行性和所提算法的有效性。

Abstract

A technical idea was innovatively proposed that uses image deep learning to solve the problem of radio signal recognition.First

the radio signal was transformed into a two-dimensional picture

and the radio signal recognition problem was transformed into the object detection problem in the field of image recognition.Then

the advanced achievements about image recognition were used to improve the intelligence and ability of radio signal recognition in complex electromagnetic environment.Based on the proposed idea

a novel radio signal recognition algorithm named RadioImageDet was proposed.The experimental results show that the algorithm can effectively identify the waveform types and time/frequency coordinates of radio signals.After training and testing on the self-collected data set with 12 types and 4 740 samples

the accuracy reaches 86.04% and the mAP value reaches 77.72

while the detection time is only 33 ms on the medium configured desktop computer.

关键词

Keywords

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