基于循环谱的水声通信信号辐射源个体识别  

Specific emitter identification of the radiation of underwater acoustic communication signal based on cyclic spectrum

作　　者：张亮[1,2,3,4] 张海刚 孟春霞[4] ZHANG Liang;ZHANG Haigang;MENG Chunxia(National Key Laboratory of Underwater Acoustic Technology,Harbin Engineering University,Harbin 150001,Heilongjiang,China;Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University),Ministry of Industry and Information Technology,Harbin 150001,Heilongjiang,China;College of Underwater Acoustic Engineering,Harbin Engineering University,Harbin 150001,Heilongjiang,China;Dalian Scientific Test and Control Technology Institute,Dalian 116013,Liaoning,China)

机构地区：[1]哈尔滨工程大学水声技术全国重点实验室,黑龙江哈尔滨150001 [2]工业和信息化部海洋信息获取与安全工信部重点实验室(哈尔滨工程大学),黑龙江哈尔滨150001 [3]哈尔滨工程大学水声工程学院,黑龙江哈尔滨150001 [4]大连测控技术研究所,辽宁大连116013

出　　处：《声学技术》2025年第1期13-20,共8页Technical Acoustics

基　　金：装备预研项目(50916020303)。

摘　　要：随着无线通信技术的快速发展,无线通信系统的通信安全也面临着前所未有的挑战。如何有效实现在非介入条件下进行水声通信物理层信号的识别分析越发重要。文章提出了一种基于循环谱特征的水声通信信号辐射源个体识别方法,采用具有不同滚降因子的根升余弦滤波器表征不同的水声通信信号辐射源个体,设计了适合于水声通信信号的轻量型神经网络模型MobilenetV3-small,将循环谱图作为网络输入,可实现5个二进制相移键控调制辐射声源信号的识别。仿真结果表明,与传统的卷积神经网络VGG16相比,文中所提方法在运行速度、参数量和损失率等方面表现更好,验证了该个体识别算法的有效性。The rapid development of wireless communication technology has brought unprecedented challenges to the communication security of wireless communication systems.Effectively identifying and analyzing the physical layer signals of underwater acoustic communication under non-intervention conditions is becoming increasingly important.A method for individual identification of radiation sources in underwater acoustic communication signals based on cyclic spectral features is proposed in this paper.Different roll-off factors of root raised cosine filters are used to characterize individual underwater acoustic communication signal radiation sources.A lightweight neural network model,MobilenetV3 small,suitable for underwater acoustic communication signals,is designed.The cyclic spectrum is used as the network input to achieve specific emitter identification of 5 binary phase shift keying(BPSK)modulated radiated sound source signals.Simulation results demonstrate that the proposed method outperforms the traditional convolutional neural network VGG16 in terms of running speed,parameter quantity,and loss rate,thus proving the effectiveness of the algorithm.

关 键 词：水声通信信号辐射源 循环谱特征 MobilnetV3-small深度学习网路 个体识别 

分 类 号：TB56[交通运输工程—水声工程]