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。
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 prob- lem was transformed into the object detection problem in the field of image recognition. Then, the advanced achieve- ments 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.
作者
周鑫
何晓新
郑昌文
ZHOU Xin;HE Xiaoxin;ZHENG Changwen(Science&Technology on Integrated Information System Laboratory,Institute of Software Chinese Academy of Sciences,Beijing 100190,China)
出处
《通信学报》
EI
CSCD
北大核心
2019年第7期114-125,共12页
Journal on Communications
基金
国防科技创新特区基金资助项目~~
关键词
无线电信号识别
深度学习
射频机器学习
卷积神经网络
图像目标检测
radio signal recognition
deep learning
radio frequency machine learning
convolutional neural network
image object detection
