基于信号稀疏分解的水下回波分类

针对表面起伏不平的水下底质回波分类效果差的问题,提出一种新颖的基于信号稀疏分解理论的水下底质回波特征提取方法。本方法并不使用通用时频字典,而是针对回波分类这一中心任务直接采用回波训练样本集作为字典,将水下回波信号在该字典上进行稀疏分解,然后提取出回波信号的类别能量特征。对水下钴结壳等三类底质回波分类实验表明,基于信号稀疏分解的类别能量特征的fisher分布明显优于小波域模极大值边缘特征和奇异值特征,从而显著提高了水下回波的分类效果。研究结论:在回波特征提取阶段,采用回波样本作为信号表达字典是可行的,同时由回波样本字典引入的回波类别信息将有助于获取更优的回波特征。 

Features of underwater echo extraction based on signal sparse decomposition

It is known that classification of underwater materials by echo is not so satisfactory while surface of the materials is heavily uneven.A new method,which is based on the theory of signal sparse decomposition,to extract novel features of underwater echo has been proposed in this paper.With this method,sets of training echo samples are used as a dictionary directly instead of the common time frequency dictionary while decomposing underwater echo and abstracting the classified energy feature of the echo.Experiments on three kinds of bottom materials including the cobalt crust show that the fisher distribution with this method is superior to that of edge features and of Singular Value Decomposition features in wavelet domain.It means no doubt that much better classification result of underwater materials can be obtained with the classified energy features than the other two.It is concluded that echo samples used as a dictionary is feasible and the classified information of echo introduced by this dictionary can help to obtain better echo features.