【摘要】 考虑水声信号的非平稳性及时变性,对信号进行小波包分解。不同的小波包基可以反映不同的信号特性,基于距离准则,求取小波包局域判别基,在局域判别基的基础上,提出通过求取局域判别基的各子空间的能量,形成特征矢量的特征提取方法。利用Fisher准则函数进行特征选择,得到识别特征矢量,针对识别特征矢量设计神经网络分类器,对三类目标进行分类,验证实验表明,基于这种方法提取的识别特征矢量在水声目标分类识别中是有效的。更多还原

【Abstract】 For non-stationary and time-varying underwater sound signals,wavelet packet transform is used. The character istic of every wavelet packet basis is different,which can express the main feature of a si-gnal. The local discriminant basis (LDB) can be calculated based on the distance criterion. A feature extraction method is proposed. The feature vector,which expresses the energy of sub-space in LDB,is obtained. Feature choice is done using Fisher criterion. A neural network target classifier is designed. And the classification experiment for three different classes of targets has been done. The results of experi-ment show that the feature extraction and choice method is useful.更多还原