摘要
针对水下目标材料的分类问题,提出了一种基于多域特征提取的水下目标材料分类方法.将目标回波信号的时域自回归(AR)系数、倒谱域特征、时频联合域的谱峰及谱峰频率相结合,实现了对金属、岩石、塑料、橡胶4类水下目标材料的分类,并利用消声水池的实测数据来验证该方法的有效性.结果表明,对于4类材料的仿真数据,所提方法的分类正确率均高于80%,且利用多域特征的效果明显优于利用单一特征.对于塑料和金属的实测数据,在信混比不小于0 dB时,所提方法的分类正确率可达80%以上.该方法对目标大小或形状等几何特征的差异具有较好的鲁棒性.
To solve the problem of the classification of underwater target materials,an underwater target material classification method based on multi-domain feature extraction was proposed.By combining four features of the target echo signal including the auto regressive(AR)coefficients in the time domain,the cepstrum domain feature,the spectral peak and the frequency in the time-frequency joint domain,the classification of four underwater target materials such as metal,rock,plastic and rubber was achieved.The measured data from the anechoic tank were used to verify the effectiveness of the method.The results show that for the simulated data of four materials,the classification accuracy of the proposed method is higher than 80%,and the classification performance using multi-domain features is obviously better than that using a single feature.For the measured data of plastic and metal,the classification accuracy of the proposed method is more than 80%when the signal-to-reverberation ratio is no less than 0 dB.This method is robust to the differences in geometric characteristics of the target such as the size or shape.
作者
韩宁
王祎庭
Han Ning;Wang Yiting(School of Information Science and Engineering,Southeast University,Nanjing 211189,China)
出处
《东南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2024年第3期781-788,共8页
Journal of Southeast University：Natural Science Edition
基金
国家自然科学基金资助项目(11874109)
中央高校基本科研业务费专项资金资助项目(2242023K30003,2242023K30004)。
关键词
水下目标
回波
特征提取
材料分类
underwater target
echo signal
feature extraction
material classification
