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

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。

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.