基于卷积和循环网络的水声信号联合特征表示和识别方法  

Joint Feature representation and recognition method of underwater acoustic signal based convolutional and loop network

作　　者：杜柏润 章博 DU Bai-run;ZHANG Bo(School of Economics and Management,Dalian University of Technology,Dalian 115023,China;Dalian Scientific Test and Control Technology Institute,Dalian 116013,China)

机构地区：[1]大连理工大学,辽宁大连116023 [2]大连测控技术研究所,辽宁大连116013

出　　处：《舰船科学技术》2023年第15期107-110,共4页Ship Science and Technology

摘　　要：传统的水声信号识别方法是将特征提取和分类识别分开进行处理的,影响了水声信号识别的整体性能。本文根据水声信号的特点,结合一维卷积网络(1DCNN)的卷积运算、时间平移不变性和门控循环网络(GRU)内部充分考虑时序相关性的记忆能力等优势,将一维卷积网络和门控循环网络进行串联中并对网络参数和模型结构进行优化,自适应提取特征给出分类结果,并与单独使用1DCNN和GRU网络模型的分类性能进行对比。结果表明,本文提出的网络对水声信号的识别准确率最高。The traditional underwater acoustic signal recognition method deals with feature extraction and classification separately,the overall performance of underwater acoustic signal recognition is affected.In this paper,based on the characteristics of underwater acoustic signal,we combine the advantages of convolution operation,time-shift invariance of one-dimensional convolutional network,and memory ability of gated loop network that fully consider temporal correlation,we connect the one-dimensional convolutional network and the gated loop network in series,and optimize the network parameters and model structure,adaptive feature extraction and give classification.Compared with the classification performance of 1DCNN and GRU network model alone,the result shows that the proposed network has the highest recognition accuracyforunderwateracoustic signal.

关 键 词：水声信号识别 一维卷积网络 门控循环网络 

分 类 号：TN911[电子电信—通信与信息系统]