基于双路射频指纹卷积神经网络与特征融合的雷达辐射源个体识别
Radar Emitter Identification Based on Dual Radio Frequency Fingerprint Convolutional Neural Network and Feature Fusion
肖易寒 1王博煜 1于祥祯 2蒋伊琳1
作者信息
- 1. 哈尔滨工程大学先进船舶通信与信息技术工业和信息化部重点实验室 哈尔滨 150000
- 2. 上海无线电设备研究所 上海 201100
- 折叠
摘要
为实现雷达辐射源个体识别不受信号参数、调制方式的影响,该文提出基于双路射频指纹卷积神经网络(Dual RFF-CNN2)和特征融合的雷达辐射源个体识别方法.首先从接收的射频信号中提取原始I/Q(Raw-I/Q)信号;其次分别对Raw-I/Q两路信号进行轴向积分双谱(AIB)和围线积分双谱(SIB)降维以构建双谱积分矩阵;最后将Raw-I/Q信号及双谱积分矩阵共同送入Dual RFF-CNN2网络并进行特征融合以实现雷达辐射源个体识别.实验结果表明,该方法具有较高的识别准确率,提取的"指纹特征"具备稳定性、鲁棒性.
Abstract
In order to achieve identification of radar emitter unaffected by signal parameters and modulation methods,a method based on Dual Radio Frequency Fingerprint Convolutional Neural Network(Dual RFF-CNN2)and feature fusion is proposed in this paper.Firstly,Raw-In-phase/Quadrature(Raw-I/Q)signals are extracted from the received radio frequency signals.Secondly,Axially Integral Bispectrum(AIB)and Square Integral Bispectrum(SIB)dimensionality reduction are performed separately on Raw-I/Q signals to construct the bispectrum integration matrix.Finally,both the Raw-I/Q signals and the bispectrum integration matrix are fed into the Dual RFF-CNN2 network for feature fusion to achieve identification of radar emitter.Experimental results demonstrate that this method achieves high identification accuracy,and the extracted"fingerprint features"exhibit stability and robustness.
关键词
雷达辐射源个体识别/双路射频指纹卷积神经网络/特征融合/指纹特征/原始I/Q信号Key words
Radar emitter identification/Dual Radio Frequency Fingerprint Convolutional Neural Network(Dual RFF-CNN2)/Feature fusion/Fingerprint feature/Raw-In-phase/Quadrature(Raw-I/Q)signal引用本文复制引用
出版年
2024
NETL
NSTL
万方数据