| 柳革命,孙超,刘兵,杨益新.局域判别基空间能量的水声目标特征提取[J].声学技术,2007,(6):1089~1093 |
| 局域判别基空间能量的水声目标特征提取 |
| Feature extraction based on subspace energy of local discriminant basis |
| 投稿时间：2006-11-07 修订日期：2007-03-27 |
| DOI： |
| 中文关键词: 小波包 局域判别基 Fisher准则 特征提取 模式识别 |
| 英文关键词: wavelet packet LDB Fisher criterion feature extraction pattern recognition |
| 基金项目: |
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| 摘要点击次数: 972 |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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 signal. 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. |
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