基于差分星座轨迹图的多任务802.11b/g信号识别方法  

A Multi-task 802.11b/g Signal Recognition Method Based on Differential Constellation Trace Figure

作　　者：谢星丽 谢跃雷[1,2] XIE Xingli;XIE Yuelei(School of lnformation and Communication,Guilin University of Electronic Technology,Guilin 541001,China;National and Local Joint Engineering Research Centre for Satellite Navigation,Positioning and Location Services,Guilin 541001,China)

机构地区：[1]桂林电子科技大学信息与通信学院,广西桂林541001 [2]卫星导航定位与位置服务国家地方联合工程研究中心,广西桂林541001

出　　处：《电讯技术》2023年第11期1771-1778,共8页Telecommunication Engineering

基　　金：广西科技重大专项(桂科AA21077008)。

摘　　要：针对802.11b/g无线信号的调制方式识别和辐射源个体识别问题,提出了一种基于差分星座轨迹图的多任务卷积神经网络识别方法。将调制识别和辐射源个体识别看作两个相互关联的学习任务,通过共享参数的深度网络提取差分星座轨迹图的特征,并由结构不同的两个分支网络进行分类识别,同时对这两个任务进行联合优化训练并相互促进学习。实验中使用6个不同的路由器进行验证,结果表明相比于单任务模型的识别方法,多任务模型所用的训练时长和模型所占内存均比两个单任务模型之和少,同时对辐射源个体、调制方式的识别率分别平均提高了1.17%和3%。For the modulation recognition and emitter individual recognition of 802.11b/g wireless signal,a multi-task convolution neural network recognition method based on differential constellation trace figure is presented.The modulation recognition and emitter individual recognition are regarded as two interrelated learning tasks.The characteristics of the differential constellation trace figure are extracted through the depth network with shared parameters,and classified and recognized by two branch networks with different structures.At the same time,the two tasks are jointly optimized and mutually promoted to learn.In the experiment,six different routers are used for verification.The results show that compared with the recognition method of single task model,the multi-task model takes less training time and memory than the sum of the two single task models.Meanwhile,the recognition rate of individual emitter and modulation mode is improved by 1.17%and 3%on average respectively.

关 键 词：辐射源识别 调制方式识别 差分星座轨迹图 多任务学习 深度学习 

分 类 号：TN911.7[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程]