改进的深度残留收缩网络,用于识别未知干扰的智能干扰

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Published on: December 15, 2023

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Xiaojun Wu^1,2, Yibo Zhou^1,2, Daolong Wu³

¹School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)

|September 28, 2023

概括

一种名为AFUCR-SNRSN的新型号,在杂的战场环境中增强了飞行特设网络 (FANET) 的干扰识别. 它可以实现对已知和未知信号的高精度,即使在低干扰噪声比率下也是如此.

科学领域:

电气工程电气工程
计算机科学计算机科学
信号处理信号处理

背景情况:

飞行特设网络 (FANET) 在复杂的战场上,在手动特征提取,低噪声识别率和识别未知的干扰信号方面扎.
现有的方法在具有挑战性的信号环境中缺乏稳定性,阻碍了有效的通信管理和安全.

研究的目的:

开发一个先进的模型来准确识别FANET中的通信干扰信号,解决噪音条件和未知信号类型的局限性.
为了提高FANET内部干扰信号识别的稳定性和准确性,以提高运营效率.

主要方法:

引入一个简单的非局部校正收缩 (SNCS) 模块,具有自适应值和基于局部重要性的聚合 (LIP),以增强信号特征和减少噪声.
开发一个关节损失函数,将交叉和中心损失结合起来,用于有效的模型训练.
对未知类识别的接受因子的建议,集成到基于接受因子的未知类识别简化非局部残留收缩网络 (AFUCR-SNRSN) 模型中.

主要成果:

与其他方法相比,AFUCR-SNRSN模型显示出更高的识别精度,特别是在低干扰噪声比 (JNR) 场景中.
已知干扰信号的准确性增加了4-9%,在-6 dB JNR下达到99%.
识别未知的干扰信号的错误阳性率 (FPR) 显著降低至9%.

结论:

AFUCR-SNRSN模型有效地解决了FANET中干扰信号识别的挑战,特别是在不利的条件下.
拟议的模型为已知和未知的干扰信号识别提供了强大的解决方案,提高了FANET的安全性和可靠性.
适应值,功能增强和关节损失功能有助于在复杂的电磁环境中实现该模型的高性能.

关键词:

有关OCSVM的OCSVM是什么在SNCS中,SNCS是最重要的.通信干扰通信干扰新类拒绝新类的拒绝软值是一个软值.

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