基于自适应特征模式分解与联合卷积的UUV辐射噪声识别方法  

UUV radiated noise identification method based on adaptive feature modal decomposition and joint convolutional network

作　　者：肖启阳 黄澳飞 金勇[1] 魏倩[1] XIAO Qi-yang;HUANG Ao-fei;JIN Yong;WEI Qian(School of Artificial Intelligence,Henan University,Zhengzhou 450046,China)

机构地区：[1]河南大学人工智能学院,郑州450046

出　　处：《控制与决策》2025年第1期155-161,共7页Control and Decision

基　　金：国家自然科学基金项目(61771006);河南省科技攻关项目(HDXJJG2021-140,22A416004,HDXJJG2023-064,231111212500(省重点研发项目))。

摘　　要：针对水下小型UUV难以检测识别问题,提出基于自适应特征模式分解与联合卷积网络的UUV辐射噪声识别方法.首先,采用自适应特征模式分解(AFMD)对信号进行处理,获取一系列分解分量,根据基尼指数(GiNi index)选取最优分量进行重构;然后,对重构后的信号进行连续小波变换,获取不同类型辐射噪声的二维时频图;最后,在频率动态卷积模块和SGE (spatial group-wise enhance)模块基础上,引入特征融合模块构建联合卷积神经网络(JCNN),利用所设计网络提取二维时频图特征,实现水下无人潜水器辐射噪声分类.实验结果表明,所提出方法能够对水下UUV辐射噪声进行识别,且识别率较高.Aiming at the detection and recognition challenges of underwater unmanned underwater vehicle(UUV)radiation noise,a proposed method combining adaptive feature modal decomposition(AFMD)with a joint convolutional neural network(JCNN)is proposed.Firstly,AFMD is applied to the signal to obtain a set of decomposition components,followed by reconstruction based on the GiNi index to select the most optimal component.Then,the reconstructed signal undergoes continuous wavelet transformation,producing 2D time-frequency maps representative of distinct radiation noise categories.The methodology incorporates a feature fusion module within the frequency dynamic convolution module and SGE module to establish the JCNN.This network adeptly extracts features from the 2D time-frequency maps,facilitating the classification of UUV radiation noise.Experimental results demonstrate the effectiveness of the proposed method in accurately identifying underwater UUV radiation noise,achieving a notably high recognition rate.

关 键 词：水下辐射噪声 自适应特征模式分解 联合卷积神经网络 噪声识别 

分 类 号：TN911.6[电子电信—通信与信息系统] TB566[电子电信—信息与通信工程] TP181[交通运输工程—水声工程]