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
水下爆炸试验采集的数据量庞大并掺杂大量无用数据,为保护数据不受爆炸的影响,试验时需要优先将关键数据识别并存储.针对此,文中提出一种将特征提取方法和深度学习模型相结合的关键信号识别模型,以提升对关键信号识别的准确率.首先,研究了不同预处理方法对水下爆炸加速度信号趋势项的去除效果,并用已有试验结果证明小波包分解法、经验模态分解法和高通滤波法可较好地提升模型的识别性能;其次,为使提取的特征更有利于区分爆炸段与非爆炸段,提出一种针对水下爆炸加速度信号的基于类间方差比的特征提取方法,基于水下爆炸实测加速度信号数据可知,相比于Log Mel特征,文中提出的特征用K-means方法分类准确率提升约 4.92%;最后,引入添加SE-Res2Block模块的ECAPA-TDNN模型,该模型具有更好的识别准确率,以文中提出的特征作为输入,识别准确率达 99.31%.
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 actual measured 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引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据