面向无线电数字孪生的多感知节点卷积融合身份识别算法

doi:10.11959/j.issn.1000-436x.2023227

通信学报 ›› 2023, Vol. 44 ›› Issue (11): 13-24.doi: 10.11959/j.issn.1000-436x.2023227

• 专题：面向泛在物联的普适感知与智能识别关键技术 • 上一篇

面向无线电数字孪生的多感知节点卷积融合身份识别算法

魏国峰, 丁国如, 焦雨涛, 徐以涛, 郭道省, 汤鹏

陆军工程大学通信工程学院，江苏南京 210001

修回日期:2023-11-07 出版日期:2023-11-01 发布日期:2023-11-01
作者简介:魏国峰（1995− ），男，辽宁葫芦岛人，陆军工程大学博士生，主要研究方向为电磁空间数字孪生、机器学习、辐射源个体识别技术
丁国如（1986− ），男，河南新乡人，博士，陆军工程大学教授，主要研究方向为电磁频谱深度感知及高效利用理论、方法与应用
焦雨涛（1992− ），男，江苏南京人，博士，陆军工程大学讲师，主要研究方向为无线网络、机器学习、区块链技术
徐以涛（1971− ），男，江苏南京人，博士，陆军工程大学教授，主要研究方向为无线通信、数字信号处理技术
郭道省（1973− ），男，江苏南京人，博士，陆军工程大学教授，主要研究方向为卫星通信、无人机通信技术
汤鹏（1997− ），男，江苏盐城人，陆军工程大学博士生，主要研究方向为辐射源个体识别、机器学习、辐射源行为识别技术
基金资助:
国家自然科学基金资助项目(U20B2038);国家自然科学基金资助项目(62231027);国家自然科学基金资助项目(62171462);国家自然科学基金资助项目(61931011);国家自然科学基金资助项目(62101594)

Multi-sensing node convolution fusion identity recognition algorithm for radio digital twin

Guofeng WEI, Guoru DING, Yutao JIAO, Yitao XU, Daoxing GUO, Peng TANG

College of Communication Engineering, Army Engineering University of PLA, Nanjing 210001, China

Revised:2023-11-07 Online:2023-11-01 Published:2023-11-01
Supported by:
The National Natural Science Foundation of China(U20B2038);The National Natural Science Foundation of China(62231027);The National Natural Science Foundation of China(62171462);The National Natural Science Foundation of China(61931011);The National Natural Science Foundation of China(62101594)

摘要/Abstract

摘要：

电磁空间是赋能统筹海、陆、空、太空、网络的重要纽带，电磁目标识别为电磁空间的孪生构建提供了重要的无线电目标身份信息，使其可以在数字空间描述、刻画物理空间的电磁目标身份态势。然而，单个感知节点易受到干扰、识别性能受限，错误的识别结果将会为孪生提供虚实不一致的身份信息。为此，面向电磁空间无线电数字孪生的需求，首先构建了面向无线电数字孪生的无线电目标识别框架，然后提出了面向无线电数字孪生的多感知节点卷积神经网络个体身份融合识别算法。通过在实际场景中部署多节点识别网络，相比于距离最近的单感知节点，识别性能提高了6.29%，提供了更加准确的个体身份信息。

关键词: 卷积神经网络, 无线电数字孪生, 多感知节点, 身份融合识别

Abstract:

Electromagnetic space is an important link to empower and coordinate sea, land, air, space and network.Electromagnetic target recognition provides important radio target identity information for the twin construction of electromagnetic space, so that it can describe and depict the identity situation of electromagnetic targets in digital space.However, a single sensing node is vulnerable to interference, and its recognition performance is limited.Wrong recognition results will provide radio digital twin with conflicting identity information.Therefore, based on the requirements of radio digital twin in electromagnetic space, a radio target recognition framework for radio digital twin was constructed and a multi-sensing node convolution neural network individual identity fusion recognition algorithm was proposed.Compared with the nearest single sensing node, the recognition performance is improved by 6.29% by deploying the multi-node recognition network in the actual scene, which provides more accurate individual identity information.

Key words: convolution neural network, radio digital twin, multi-sensing node, identity fusion recognition

中图分类号:

TN92

魏国峰, 丁国如, 焦雨涛, 徐以涛, 郭道省, 汤鹏. 面向无线电数字孪生的多感知节点卷积融合身份识别算法[J]. 通信学报, 2023, 44(11): 13-24.

Guofeng WEI, Guoru DING, Yutao JIAO, Yitao XU, Daoxing GUO, Peng TANG. Multi-sensing node convolution fusion identity recognition algorithm for radio digital twin[J]. Journal on Communications, 2023, 44(11): 13-24.

图/表 14

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分层	内核大小
卷积层0+ReLU	3×1 000×1,32
池化层1（Max-Pooling）	2×1
卷积层1+ReLU	3×1,64
池化层2（Max-Pooling）	2×1
卷积层2+ReLU	3×1,128
池化层3（Max-Pooling）	2×1
展平层（Flatten）	（None,128）
全连接层1+ReLU	128
全连接层2+ReLU	128
Softmax层	10

面向无线电数字孪生的多感知节点卷积融合身份识别算法

Multi-sensing node convolution fusion identity recognition algorithm for radio digital twin

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