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

被引：0
|
作者：
Wei G. [1 ]
Ding G. [1 ]
Jiao Y. [1 ]
Xu Y. [1 ]
Guo D. [1 ]
Tang P. [1 ]
机构：
[1] College of Communication Engineering, Army Engineering University of PLA, Nanjing
来源：
Tongxin Xuebao/Journal on Communications | 2023年 / 44卷 / 11期
基金：
中国国家自然科学基金;
关键词：
convolution neural network; identity fusion recognition; multi-sensing node; radio digital twin;
D O I：
10.11959/j.issn.1000-436x.2023227
中图分类号：
学科分类号：
摘要：
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. © 2023 Editorial Board of Journal on Communications. All rights reserved.
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页码：13 / 24
页数：11
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