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

Abstract

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

CLC Number:

TN92

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.

Figures/Tables 14

References 26

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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

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

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Figures/Tables 14

References 26

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