Radio signal recognition based on image deep learning

Xin ZHOU; Xiaoxin HE; Changwen ZHENG

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

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Radio signal recognition based on image deep learning

Papers | 更新时间：2024-06-05

- Radio signal recognition based on image deep learning
- Journal on Communications Vol. 40, Issue 7, Pages: 114-125(2019)
- 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：
- 基金信息：
  
  The Special Fund for National Defense Technology Innovation
- DOI：10.11959/j.issn.1000-436x.2019167
  CLC： TN971
- Online First：2019-07，
  
  Published：25 July 2019
- 稿件说明：
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Xin ZHOU, Xiaoxin HE, Changwen ZHENG. Radio signal recognition based on image deep learning[J]. Journal on Communications, 2019, 40(7): 114-125.
DOI：

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

references

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