Feature extraction from underwater signals using wavelet packet transform

Processing underwater acoustic signals for monitoring and classification are difficult problems that have recently attracted attention in the field of underwater signal processing. For these purposes, it is necessary to use a method which could be able to extract the useful information about the processed data. In this paper, an algorithm of extracting feature from radiated noise of underwater targets based on wavelet packet transform is presented. Energy features in each frequency band are extracted after four-stage wavelet packet decomposition on signals and low-dimension feature vectors are obtained. The extracted features are passed into the RBF network classifier. The results show that the wavelet packet transform could enhance the fractal features of the signals and reduce the number of dimensions of the feature space. It can efficiently classify underwater targets.