乘性噪声干扰下无线激光通信信号异常检测研究  

Research on abnormal detection of wireless laser communication signal under multiplicative noise interference

作　　者：杨浩 张帆 许绘香 黄继海 YANG Hao;ZHANG Fan;XU Huixiang;HUANG Jihai(School of Information Engineering,Zhengzhou Institute of Technology,Zhengzhou 450044,China)

机构地区：[1]郑州工程技术学院信息工程学院,郑州450044

出　　处：《激光杂志》2024年第11期151-155,共5页Laser Journal

基　　金：河南省科技攻关项目(No.232102210046);河南省本科高校虚拟教研室立项建设项目(No.2022394);河南省本科高校新工科新形态教材立项建设项目(No.2023395);河南省本科高校第二批省级重点现代产业学院拟立项建设项目(No.2023280)。

摘　　要：无线激光通信环境复杂,对无线激光通信信号的可靠性提出了严峻的挑战,为此提出乘性噪声干扰下无线激光通信信号异常检测方法,以及时采取处理措施,保证无线激光通信质量。融合卷积神经网络和机器学习方法,构建基于深度学习技术的无线激光通信信号异常识别模型,判断异常是否存在。根据识别所得的无线激光通信信号异常特点,估计中心频点、脉冲周期、扫频速率等异常参数,完成无线激光通信信号异常检测。实验结果显示,不同信号异常的归一化识别指数高达0.98、1、1、0.99、0.99,且归一化均方根误差较低,可证明所提方法的检测精度较高,具备优越的检测性能。The complex environment of wireless laser communication poses a serious challenge to the reliability of wireless laser communication signals.Therefore,a method for detecting abnormal wireless laser communication signals under multiplicative noise interference is proposed to take timely processing measures and ensure the quality of wireless laser communication.Integrating convolutional neural networks and machine learning methods,a wireless laser communication signal anomaly recognition model based on deep learning technology is constructed to determine the existence of anomalies.Based on the identified abnormal characteristics of wireless laser communication signals,estimate abnormal parameters such as center frequency point,pulse period,and scanning rate to complete wireless laser communication signal anomaly detection.The experimental results show that the normalized recognition indices for different signal anomalies are as high as 0.98,1,1,0.99,and 0.99,and the normalized root mean square error is low,which proves that the proposed method has high detection accuracy and superior detection performance.

关 键 词：无线激光通信信号 卷积神经网络 乘性噪声干扰 异常检测 

分 类 号：TN919[电子电信—通信与信息系统]