基于EfficientNet-WGANomaly的雷达辐射源个体开集识别  

Specific Radar Emitter Identification in Open-Set Environment Based on EfficientNet-WGANomaly

作　　者：孙佳杰 崔良中[1] 吕晓[1] 牛雅萌 SUN Jiajie;CUI Liangzhong;LYU Xiao;NIU Yameng(School of Electronic Engineering Naval University of Engineering,Wuhan 430000 China)

机构地区：[1]海军工程大学电子工程学院,武汉430000

出　　处：《电光与控制》2024年第11期34-40,共7页Electronics Optics & Control

基　　金：湖北省自然科学基金(2019CEB627)。

摘　　要：现代战场电磁环境日趋复杂,辐射源种类繁多,传统闭集环境下雷达辐射源的识别方法应用于开集环境上识别精度较低、鲁棒性较差。为了有效解决辐射源个体开集识别问题,提高辐射源个体识别的精度,借鉴图像异常检测的思想,提出了基于EfficientNet-WGANomaly的雷达辐射源个体开集识别方法。采用短时傅里叶变换对雷达辐射源信号进行时频特征转换,将一维信号数据转换成二维时频图,利用EfficientNet-WGANomaly模型对二维时频图进行特征重构和图像重构,未知信号重构前后的特征及图像差异性较大,利用图像异常检测的差异性区分已知信号和未知信号,并对已知信号进行个体识别。仿真实验表明,提出的方法有效解决了雷达辐射源个体开集识别的问题。The electromagnetic environment of modern battlefield is becoming more and more complex and there are many kinds of radar emitter.The traditional radar emitter recognition method in closed set environment has low identification accuracy and poor robustness when applied to open set environment.In order to effectively solve the problem of individual emitter open set identification and improve the accuracy of individual emitter identification an open set identification method of radar emitter based on EfficientNet-WGANomaly is proposed.Firstly the Short-Time Fourier Transform(STFT)is used to convert the time-frequency feature of the radar emitter signal and the one-dimensional signal data is converted into two-dimensional image data.The EfficientNet-WGANomaly model is used to perform feature reconstruction and image reconstruction of the converted time-frequency feature.Since the feature and image of unknown signals change greatly after reconstruction the known signals and unknown signals are screened by using the reconstruction difference of the two parts and the known signals are classified and identified.Simulation results show that the proposed method can effectively solve the problem of specific radar emitter identification in open set environment.

关 键 词：辐射源个体识别 开集识别 短时傅里叶变换 EfficientNet-WGANomaly 

分 类 号：TN957[电子电信—信号与信息处理]