Features of underwater echo extraction based on signal sparse decomposition

In order to better realize sound echo recognition of underwater materials with heavily uneven surface, a features abstraction method based on the theory of signal sparse decomposition has been proposed. Instead of the common time frequency dictionary, sets of training echo samples are used directly as dictionary to realize echo sparse decomposition under L1 optimization and abstract a kind of energy features 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 （SVD） features in wavelet domain. It means no doubt that much better classification result of underwater bottom materials can be obtained with the proposed energy features than the other two. It is concluded that echo samples used as a dictionary is feasible and the class information of echo introduced by this dictionary can help to obtain better echo features.