摘要
研究了基于小波包变换和Fisher线性分类器的水下目标识别方法。小波包是在小波变换的基础上发展起来的时频分析方法,能够对非平稳信号提供更丰富的时频信息。通过对水下目标辐射噪声信号进行小波包分解,提取小波包分解的终端节点的熵值作为特征矢量,应用Fisher线性分类器设计的分段线性分类器对水下目标进行分类识别。仿真结果表明,以小波包熵作为特征矢量的分类方法具有较高的识别正确率。
A method for underwater target recognition based on wavelet packet transform and fisher linear classifier is studied. On the basis of wavelet transform, the wavelet packet transform is developed. It can offer plentiful time-frequency information for nonstationary signals. Firstly, the radiated noise of underwater target is decomposed by wavelet packet. Secondly, the entropy of terminal nodes through wavelet packet is served as feature vectors. Lastly, the piecewise linear classifier which is designed based on Fisher linear classifier is applied for underwater target recognition. Simulation results show that the classification method which uses wavelet packet entropy as feature vectors possesses higher recognition cor-rect ratio.
出处
《计算机工程与应用》
CSCD
2014年第1期215-217,231,共4页
Computer Engineering and Applications
关键词
目标识别
小波包变换
小波包熵
Fisher线性分类器
分段线性分类器
target recognition
wavelet packet transform
wavelet packet entropy
Fisher linear classifier
piecewise lin-ear classifier
