信号处理2025，Vol.41Issue(3)：524-532,9.DOI:10.12466/xhcl.2025.03.010
基于通道分组注意力机制的水声目标识别网络
Underwater Acoustic Target Recognition Network Based on Channel Grouping Attention Mechanism
摘要
Abstract
This paper addresses the issue of inadequate utilization of local channel information in traditional object recog-nition networks by proposing a featured channel grouping attention mechanism.This mechanism is integrated with re-sidual convolutional neural networks to create an effective feature extraction network.Initially,the features are seg-mented along the channel dimension,resulting in multiple sub-features.Within these sub-features,the significance of each channel is highlighted,and appropriate weights are assigned.Channel rearrangement is then applied to form sub-feature groups,facilitating enhanced information exchange among the overall channels.Following this,the average pooled feature map of the sub-features is utilized as a representative,allowing for further information exchange to en-hance and amalgamate both the overall and local channel information of the features.To further enhance the recognition performance of the network,this paper uses the Low-Frequency Analysis and Recording(LOFAR)spectrum and the Mel spectrum of underwater acoustic target radiation noise as inputs for the network model.It constructs a feature fusion network using an autoencoder to achieve information exchange between different features.The time-frequency features of the two input signals are deeply fused to improve the feature representation of the information conveyed by the signal.Experimental validation using the ShipsEar dataset shows that the improved attention mechanism proposed in this paper increases recognition accuracy by over 1.38%compared to commonly used channel attention mechanisms.The fusion of the two features for recognition enhances accuracy by 6.17%and 1.2%,respectively,compared to using the LOFAR and Mel spectra separately.关键词
水声目标识别/时频谱特征/卷积神经网络/注意力机制Key words
underwater acoustic target recognition/time-frequency spectrum feature/convolution neural network/at-tention mechanism分类
通用工业技术引用本文复制引用
王桡,鄢社锋,毛琳琳,于佳平..基于通道分组注意力机制的水声目标识别网络[J].信号处理,2025,41(3):524-532,9.基金项目
国家自然科学基金(62192711,62371447) The National Natural Science Foundation of China(62192711,62371447) （62192711,62371447）
