水下无人系统学报2024，Vol.32Issue(4)：739-748,10.DOI:10.11993/j.issn.2096-3920.2023-0146
基于深度学习的水下爆炸关键信号识别方法
Deep Learning-Based Method for Key Signal Recognition during Underwater Explosions
周稹先 1洪峰 2许伟杰 2张涛 2陈峰2
作者信息
- 1. 中国科学院声学研究所东海研究站,上海,201815||中国科学院大学,北京,100049
- 2. 中国科学院声学研究所东海研究站,上海,201815
- 折叠
摘要
Abstract
The amount of data collected from underwater explosion tests is enormous,which is mixed with a large amount of useless data.To protect the data from the effects of the explosion,it is crucial to prioritize the recognition and storage of key data during the test.In response to this,a key signal recognition model that combined feature extraction methods with deep learning models was proposed to improve the accuracy of key signal recognition.Firstly,different preprocessing methods for removing trend components from underwater explosion acceleration signals were studied.Existing test results demonstrated that wavelet packet decomposition,empirical mode decomposition,and high-pass filtering could significantly enhance the model's recognition performance.Secondly,to make the extracted features more conducive to distinguishing between explosion and non-explosion segments,a feature extraction method based on the inter-class variance ratio for underwater explosion acceleration signals was proposed.According to the underwater explosion acceleration signal data,it was found that compared to Log Mel features,the proposed features improved classification accuracy by approximately 4.92%using the K-means method.Finally,the ECAPA-TDNN model incorporating the SE-Res2Block module was introduced,ensuring better recognition accuracy.With the proposed features as input,the recognition accuracy reached 99.31%.关键词
水下爆炸/特征提取/深度学习/关键信号识别Key words
underwater explosion/feature extraction/deep learning/key signal recognition分类
军事科技引用本文复制引用
周稹先,洪峰,许伟杰,张涛,陈峰..基于深度学习的水下爆炸关键信号识别方法[J].水下无人系统学报,2024,32(4):739-748,10.
