小波包熵在水下目标识别中的应用研究  

Application study on underwater target recognition based on wavelet packet entropy

作　　者：石敏 徐袭[2] 

机构地区：[1]水声对抗技术重点实验室 [2]中国人民解放军91388部队

出　　处：《计算机工程与应用》2014年第1期215-217,231,共4页Computer Engineering and Applications

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.

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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]